{ "source_file": "0930", "source_platform": "Medium", "author": "Shashank Kapadia", "title_listed": "Recommendation System in Python: LightFM", "keywords": "", "domain": "机器学习", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/recommendation-system-in-python-lightfm-61c85010ce17/", "publish_date": "2020-02-25", "corpus_stage": "", "notes": "", "id": "95ac17dcdb22", "collected_at": "2026-10-02T10:00:37", "title": "Recommendation System in Python: LightFM | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Recommendation System in Python: LightFM\nA Step-by-Step guide to building a recommender system in Python using LightFM\n[Shashank Kapadia](https://towardsdatascience.com/author/shashank%20kapadia/)\nFebruary 25, 20209 min read\n![](../images/000.jpg)\nA recommender system, or a recommendation system, can be thought of as a subclass of information filtering system that seeks to predict the best “rating” or “preference” a user would give to an item which is typically obtained by optimizing for objectives like total clicks, total revenue, and overall sales.\n> So, what is the basic principle that underlies the working of recommendation algorithms?\nThe basic principle of recommendations is that there are significant dependencies between user- and item-centric activity. For example, a user who is interested in hotels in New York City is more likely to be interested in other NYC hotels, rather than in Boston.\n## Introduction\nWhile the primary business goal of any recommender system is to provide users’ with a personalized experience; from the problem setting standpoint, we can broadly classify recommenders into two categories:\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n * **Prediction problem:** The first approach is where we would like to predict the rating value of a user-item combination with an assumption that the training data is available indicating a user’s preference for other items. Imagine m × n matrix, where m is # of users, and n is # of items, and the goal is to predict the missing (or unobserved) values.\n\n\n![](../images/001.png)\n[Image Source](https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.needpix.com%2Fphoto%2F18235%2Frating-stars-chart-poor-performance-result-statistical-success-symbols&psig=AOvVaw2TxYq_-XgJ5B65nDAszvSr&ust=1582730198563000&cd=vfe&ved=0CAIQjRxqFwoTCOiLz9D_7OcCFQAAAAAdAAAAABAE)\n * **Ranking problem:** Other times, we wish to recommend the top-k items for a particular user or determine the top-k users to target for a specific topic. This problem is also referred to as the top-k recommendation problem, and it is the ranking formulation of the recommendation problem. Think of a search engine where, depending on who is searching, you’d like to surface top-k items to serve personalized results based on their past preferences and recent activity.\n\n\n![](../images/002.png)\n[Image Source](https://www.google.com/url?sa=i&url=https%3A%2F%2Factionml.com%2Fblog%2Fpersonalized_search&psig=AOvVaw125Jk0lya25z8gCv9C4IwX&ust=1582412856268000&cd=vfe&ved=0CAIQjRxqFwoTCOCA-rXh4-cCFQAAAAAdAAAAABAJ)\n## Basics of recommendation modeling approaches\nBroadly speaking, most recommender systems leverage two types of data:\n * _Interaction data_ , such as ratings, and browsing behaviors, and\n * _Attribution information_ , about each user and items\n\n\nThe modeling approach relying on the former data is generally known as the `Collaborative Filtering` method, and approaches using the latter are referred to as `Content Base Filtering` method. There is also another category known as `Knowledge-Based` recommender system that is based on explicitly specified user requirements.\n> Of course, each of these methods has its strengths and weaknesses depending on which applications they are used for, and the amount of data available.\n`Hybrid Systems` are then used to combine the advantages of these approaches to have a robust performing system across a wide variety of applications.\n![](../images/body-6c0e9121f6e9e6db2867.jpg)\n * **Collaborative filtering methods:** These types of models use the collaborative power of the ratings provided by multiple users to make recommendations and rely mostly on leveraging either inter-item correlations or inter-user interactions for the prediction process. Intuitively, it relies on an underlying notion that two users who rate items similarly are likely to have comparable preferences for other items.\n * There are two types of methods that are commonly used in collaborative filtering:\n * **Memory-based** methods also referred to as neighborhood-based collaborative filtering algorithms, where ratings of user-item combinations are predicted based on their neighborhoods. These neighborhoods can be further defined as (1) User Based and (2) Item Based\n * In **Model-based** methods, ML techniques are used to learn model parameters within the context of a given optimization framework\n * **Content based filtering methods:** In these types of systems, the descriptive attributes of items/users are used to make recommendations. The term “content” refers to these descriptions. In content-based methods, the ratings and interaction behavior of users are combined with the content information available in the items.\n * **Hybrid methods:** In many cases, a wider variety of inputs is available; in such cases, many opportunities exist for hybridization, where the various aspects from different types of systems are combined to achieve the best of all worlds. The approach is comparable to the conventional ensemble analysis approach, where the power of multiple types of machine learning algorithms is combined to create a more robust model.\n\n\nThere is more to different types of approaches, and nuances of each; if you are interested in learning more, I would highly recommend the **Statistical Methods for Recommender Systems** book by Deepak Agarwal.\n## Primer into LightFM\nThere are many open-source recommender system frameworks and libraries available across different languages that can help you get started with your first implementation of the recommendation model.\nFor this article, we’ll explore one of them that I have found valuable in my work, and covers a wide variety of underlying algorithms for varied use-cases, known as `LightFM`.\nLightFM is a Python implementation of several popular recommendation algorithms for both implicit and explicit feedback types.\nImportantly, it allows you to incorporate both item and user metadata into the traditional matrix factorization algorithms, making it possible to generalize to new items (via item features) and new users (via user features).\nTo learn more about it, you can check its documentation [here](https://making.lyst.com/lightfm/docs/home.html).\n## Implementation\nThe complete code is available as a [Jupyter Notebook on GitHub](https://github.com/kapadias/mediumposts/blob/master/recommender/published_notebooks/recommendation_python_lightfm.ipynb).\n * **Loading data**\n * **Data inspection and preparation**\n * **Data preprocessing**\n * **Model training**\n * **Top n-recommendations**\n\n\n## Loading data\nFor this tutorial, we’ll use book reviews data by Goodreads. The datasets were collected in late 2017 from goodreads.com, where the data from users’ public shelves was scraped, i.e., everyone can see it on the web without login. User IDs and review IDs are anonymized.\n![](../images/body-a9ac84e7bc5ec3753a82.png)\nThe dataset is collected for academic use only. Please do not redistribute them or use for commercial purposes.\nThere are three groups of datasets:\n * meta-data of the books\n * user-book interactions\n * users’ detailed book reviews\n\n\nThese datasets can be merged together by matching book/user/review ids. For this tutorial, we’ll be using only the former two.\nYou can download the dataset we'reusing in this article from here:\n * Books Metadata: \n * User-Book Interactions: \n\n\npython\n\n```\n%%time\nbooks_metadata = pd.read_json('./data/goodreads_books_poetry.json', lines=True)\ninteractions = pd.read_json('./data/goodreads_interactions_poetry.json', lines=True)\n```\n\n## Data inspection & preparation\n### Books metadata\nLet’s start by inspecting the books’ metadata information. To develop a reliable and robust ML model, it is essential to get a thorough understanding of the available data.\nAs the first step, let’s take a look at all the available fields, and sample data:\npython\n\n```\nbooks_metadata.sample(2)\n```\n\n![](../images/body-95a01ca3c6d6dd89fd04.png)\nBooks Metadata (Sample)\nWhile all the available information is vital to extract contextual information to be able to train a better recommendation system, for this example, we’ll only focus on the selected fields that require minimal manipulation.\npython\n\n```\n# Limit the books metadata to selected fields\nbooks_metadata_selected = books_metadata[['book_id', \n'average_rating', 'is_ebook', 'num_pages', 'publication_year', \n'ratings_count', 'language_code']]\nbooks_metadata_selected.sample(5)\n```\n\n![](../images/body-390091886b6e75d13c30.png)\nFiltered Books Metadata (Sample)\nNow that we have the data with selected fields, next, we’ll run it through pandas profiler to perform preliminary exploratory data analysis to help us better understand the available data.\npython\n\n```\nimport pandas_profiling\nimport numpy as np\n# replace blank cells with NaN\nbooks_metadata_selected.replace('', np.nan, inplace=True)\n# not taking book_id into the profiler report\nprofile = \\\npandas_profiling.ProfileReport(books_metadata_selected[['average_rating', \n'is_ebook', 'num_pages', 'publication_year', 'ratings_count']])\nprofile.to_file('./results/profiler_books_metadata_1.html')\n```\n\n![](../images/body-eff8de8eb78d10d011fd.png)\nConsidering the results from the profiler, we’ll perform following transformations to the dataset:\n * Replace the missing value of categorical values with another value to create a new category\n * Convert bin values for numeric variables into discrete intervals\n\n\npython\n\n```\n# using pandas cut method to convert fields into discrete intervals\nbooks_metadata_selected['num_pages'].replace(np.nan, -1, inplace=True)\nbooks_metadata_selected['num_pages'] = pd.to_numeric(books_metadata_selected['num_pages'])\nbooks_metadata_selected['num_pages'] = pd.cut(books_metadata_selected['num_pages'], bins=25)\n# rounding ratings to neares .5 score\nbooks_metadata_selected['average_rating'] = books_metadata_selected['average_rating'].apply(lambda x: round(x*2)/2)\n# using pandas qcut method to convert fields into quantile-based discrete intervals\nbooks_metadata_selected['ratings_count'] = pd.qcut(books_metadata_selected['ratings_count'], 25)\n# replacing missing values to year 2100\nbooks_metadata_selected['publication_year'].replace(np.nan, 2100, inplace=True)\n# replacing missing values to 'unknown'\nbooks_metadata_selected['language_code'].replace(np.nan, 'unknown', inplace=True)\n# convert is_ebook column into 1/0 where true=1 and false=0\nbooks_metadata_selected['is_ebook'] = books_metadata_selected.is_ebook.map(\n lambda x: 1.0*(x == 'true'))\nprofile = pandas_profiling.ProfileReport(books_metadata_selected[['average_rating', 'is_ebook', 'num_pages', \n 'publication_year', 'ratings_count']])\nprofile.to_file('./results/profiler_books_metadata_2.html')\n```\n\n![](../images/body-effbd7d9fd549162bf1d.png)\n### Interactions data\nAs the first step, let’s take a look at all the available fields and sample data:\npython\n\n```\ninteractions.sample(5)\n```\n\n![](../images/body-5726539060bcefbf0211.png)\nLimit the data to only the selected fields:\npython\n\n```\n# Limit the books metadata to selected fields\ninteractions_selected = interactions[['user_id', 'book_id', 'is_read', 'rating']]\n# mapping boolean to string\nbooleanDictionary = {True: 'true', False: 'false'}\ninteractions_selected['is_read'] = interactions_selected['is_read'].replace(booleanDictionary)\nprofile = pandas_profiling.ProfileReport(interactions_selected[['is_read', 'rating']])\nprofile.to_file('./results/profiler_interactions.html')\n```\n\n![](../images/body-8df2954c95a6c1c77c79.png)\nConsidering the results from the profiler, we’ll convert is_read column to 1/0:\npython\n\n```\n# convert is_read column into 1/0 where true=1 and false=0\ninteractions_selected['is_read'] = interactions_selected.is_read.map(\n lambda x: 1.0*(x == 'true'))\n```\n\nSince we have two fields denoting interaction between a user and a book, `is_read` and `rating` — let’s see how many data points we have where the user hasn’t read the book but have given the ratings.\npython\n\n```\ninteractions_selected.groupby(['rating', 'is_read']).size().reset_index().pivot(columns='rating', index='is_read', values=0)\n```\n\n![](../images/body-0f48e532a9b679bbc56e.png)\nFrom the above results, we can conclusively infer that users with ratings >= 1 have all read the book. Therefore, we’ll use the `ratings` as the final score, drop interactions where `is_read` is false, and limit interactions from random 5000 users to limit the data size for further analysis.\n## Data preprocessing\nNow, let’s transform the available data into CSR sparse matrix that can be used for matrix operations. We will start by the process by creating books_metadata matrix which is np.float64 csr_matrix of shape ([n_books, n_books_features]) — Each row contains that book’s weights over features. However, before we create a sparse matrix, we’ll first create an item dictionary for future reference.\npython\n\n```\nitem_dict ={}\ndf = books_metadata[['book_id', 'title']].sort_values('book_id').reset_index()\nfor i in range(df.shape[0]):\n item_dict[(df.loc[i,'book_id'])] = df.loc[i,'title']\n# dummify categorical features\nbooks_metadata_selected_transformed = pd.get_dummies(books_metadata_selected, columns = ['average_rating', 'is_ebook', 'num_pages', 'publication_year', 'ratings_count', 'language_code'])\nbooks_metadata_selected_transformed = books_metadata_selected_transformed.sort_values('book_id').reset_index().drop('index', axis=1)\nbooks_metadata_selected_transformed.head(5)\n# convert to csr matrix\nbooks_metadata_csr = csr_matrix(books_metadata_selected_transformed.drop('book_id', axis=1).values)\n```\n\nNext, we’ll create an iterations matrix which is np.float64 csr_matrix of shape ([n_users, n_books]). We’ll also create a user dictionary for future use cases.\npython\n\n```\nuser_book_interaction = pd.pivot_table(interactions_selected, index='user_id', columns='book_id', values='rating')\n# fill missing values with 0\nuser_book_interaction = user_book_interaction.fillna(0)\nuser_id = list(user_book_interaction.index)\nuser_dict = {}\ncounter = 0 \nfor i in user_id:\n user_dict[i] = counter\n counter += 1\n# convert to csr matrix\nuser_book_interaction_csr = csr_matrix(user_book_interaction.values)\nuser_book_interaction_csr\n```\n\n## Model training\nIdeally, we would build, train, and evaluate several models for our recommender system to determine which model holds the most promise for further optimization (hyper-parameter tuning).\nHowever, for this tutorial, we’ll train the base model with randomly selected input parameters for demonstrations.\npython\n\n```\nmodel = LightFM(loss='warp',\n random_state=2016,\n learning_rate=0.90,\n no_components=150,\n user_alpha=0.000005)\nmodel = model.fit(user_book_interaction_csr,\n epochs=100,\n num_threads=16, verbose=False)\n```\n\n## Top n-recommendations\nFor getting back the top-n recommendations, we’ll re-purpose the code by [Aayush Agrawal](https://github.com/aayushmnit) [here](https://github.com/aayushmnit/cookbook/blob/master/recsys.py).\npython\n\n```\ndef sample_recommendation_user(model, interactions, user_id, user_dict, \n item_dict,threshold = 0,nrec_items = 5, show = True):\n \n n_users, n_items = interactions.shape\n user_x = user_dict[user_id]\n scores = pd.Series(model.predict(user_x,np.arange(n_items), item_features=books_metadata_csr))\n scores.index = interactions.columns\n scores = list(pd.Series(scores.sort_values(ascending=False).index))\n \n known_items = list(pd.Series(interactions.loc[user_id,:] \\\n [interactions.loc[user_id,:] > threshold].index).sort_values(ascending=False))\n \n scores = [x for x in scores if x not in known_items]\n return_score_list = scores[0:nrec_items]\n known_items = list(pd.Series(known_items).apply(lambda x: item_dict[x]))\n scores = list(pd.Series(return_score_list).apply(lambda x: item_dict[x]))\n if show == True:\n print (\"User: \" + str(user_id))\n print(\"Known Likes:\")\n counter = 1\n for i in known_items:\n print(str(counter) + '- ' + i)\n counter+=1\nprint(\"\\n Recommended Items:\")\n counter = 1\n for i in scores:\n print(str(counter) + '- ' + i)\n counter+=1\n```\n\nLet’s check the results:\npython\n\n```\nsample_recommendation_user(model, user_book_interaction, 'ff52b7331f2ccab0582678644fed9d85', user_dict, item_dict)\n```\n\n![](../images/body-7519b71c4a5b76c3b55d.png)\n## Closing notes\nMachine learning has become increasingly popular over the past decade, and recent advances in computational availability have led to exponential growth to people looking for ways how new methods can be incorporated to advance the field of Recommendation Systems.\nOften, we treat the Recommendation System as black-box algorithms. Still, hopefully, this post addressed to shed light on the underlying math, and intuitions behind it, and high-level code to get you started with building your first recommendation model.\nIn the next article, we’ll go one step deeper into understanding how you can evaluate the performance of these models, and tune its hyper-parameters to get more intuitive and reliable results.\n## References\n[1] \n[2] Agarwal, Deepak K.. Statistical Methods for Recommender Systems. Cambridge University Press. Kindle Edition\n[3] Charu, A. (1997). Recommender systems The Textbook. In _Communications of the ACM_ (Vol. 40)\n[4] Mengting Wan, Julian McAuley, “[Item Recommendation on Monotonic Behavior Chains](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMengtingWan%2Fmengtingwan.github.io%2Fraw%2Fmaster%2Fpaper%2Frecsys18_mwan.pdf&sa=D&sntz=1&usg=AFQjCNGGcNRW1tSZKPWO0yZsr8mj7MkWuw)”, in _RecSys’18_\n[5] Mengting Wan, Rishabh Misra, Ndapa Nakashole, Julian McAuley, “[Fine-Grained Spoiler Detection from Large-Scale Review Corpora](https://www.google.com/url?q=https%3A%2F%2Fwww.aclweb.org%2Fanthology%2FP19-1248&sa=D&sntz=1&usg=AFQjCNG8xlMi09lyuzzMI8lCW58wrBEGsQ)”, in _ACL’19_\n[6] \nThanks for reading. _If you have any feedback, please feel to reach out by commenting on this post, messaging me on_[ _LinkedIn_](https://www.linkedin.com/in/shashankkapadia/) _, or shooting me an email (shmkapadia[at]gmail.com)_\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Recommendation System in Python: LightFM\nA Step-by-Step guide to building a recommender system in Python using LightFM\n[Shashank Kapadia](https://towardsdatascience.com/author/shashank%20kapadia/)\nFebruary 25, 20209 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM.jpg)\nA recommender system, or a recommendation system, can be thought of as a subclass of information filtering system that seeks to predict the best “rating” or “preference” a user would give to an item which is typically obtained by optimizing for objectives like total clicks, total revenue, and overall sales.\n> So, what is the basic principle that underlies the working of recommendation algorithms?\nThe basic principle of recommendations is that there are significant dependencies between user- and item-centric activity. For example, a user who is interested in hotels in New York City is more likely to be interested in other NYC hotels, rather than in Boston.\n## Introduction\nWhile the primary business goal of any recommender system is to provide users’ with a personalized experience; from the problem setting standpoint, we can broadly classify recommenders into two categories:\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n * **Prediction problem:** The first approach is where we would like to predict the rating value of a user-item combination with an assumption that the training data is available indicating a user’s preference for other items. Imagine m × n matrix, where m is # of users, and n is # of items, and the goal is to predict the missing (or unobserved) values.\n\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM-1-1024x512.png)\n[Image Source](https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.needpix.com%2Fphoto%2F18235%2Frating-stars-chart-poor-performance-result-statistical-success-symbols&psig=AOvVaw2TxYq_-XgJ5B65nDAszvSr&ust=1582730198563000&cd=vfe&ved=0CAIQjRxqFwoTCOiLz9D_7OcCFQAAAAAdAAAAABAE)\n * **Ranking problem:** Other times, we wish to recommend the top-k items for a particular user or determine the top-k users to target for a specific topic. This problem is also referred to as the top-k recommendation problem, and it is the ranking formulation of the recommendation problem. Think of a search engine where, depending on who is searching, you’d like to surface top-k items to serve personalized results based on their past preferences and recent activity.\n\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM-2.png)\n[Image Source](https://www.google.com/url?sa=i&url=https%3A%2F%2Factionml.com%2Fblog%2Fpersonalized_search&psig=AOvVaw125Jk0lya25z8gCv9C4IwX&ust=1582412856268000&cd=vfe&ved=0CAIQjRxqFwoTCOCA-rXh4-cCFQAAAAAdAAAAABAJ)\n## Basics of recommendation modeling approaches\nBroadly speaking, most recommender systems leverage two types of data:\n * _Interaction data_ , such as ratings, and browsing behaviors, and\n * _Attribution information_ , about each user and items\n\n\nThe modeling approach relying on the former data is generally known as the `Collaborative Filtering` method, and approaches using the latter are referred to as `Content Base Filtering` method. There is also another category known as `Knowledge-Based` recommender system that is based on explicitly specified user requirements.\n> Of course, each of these methods has its strengths and weaknesses depending on which applications they are used for, and the amount of data available.\n`Hybrid Systems` are then used to combine the advantages of these approaches to have a robust performing system across a wide variety of applications.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM-3.jpg)\n * **Collaborative filtering methods:** These types of models use the collaborative power of the ratings provided by multiple users to make recommendations and rely mostly on leveraging either inter-item correlations or inter-user interactions for the prediction process. Intuitively, it relies on an underlying notion that two users who rate items similarly are likely to have comparable preferences for other items.\n * There are two types of methods that are commonly used in collaborative filtering:\n * **Memory-based** methods also referred to as neighborhood-based collaborative filtering algorithms, where ratings of user-item combinations are predicted based on their neighborhoods. These neighborhoods can be further defined as (1) User Based and (2) Item Based\n * In **Model-based** methods, ML techniques are used to learn model parameters within the context of a given optimization framework\n * **Content based filtering methods:** In these types of systems, the descriptive attributes of items/users are used to make recommendations. The term “content” refers to these descriptions. In content-based methods, the ratings and interaction behavior of users are combined with the content information available in the items.\n * **Hybrid methods:** In many cases, a wider variety of inputs is available; in such cases, many opportunities exist for hybridization, where the various aspects from different types of systems are combined to achieve the best of all worlds. The approach is comparable to the conventional ensemble analysis approach, where the power of multiple types of machine learning algorithms is combined to create a more robust model.\n\n\nThere is more to different types of approaches, and nuances of each; if you are interested in learning more, I would highly recommend the **Statistical Methods for Recommender Systems** book by Deepak Agarwal.\n## Primer into LightFM\nThere are many open-source recommender system frameworks and libraries available across different languages that can help you get started with your first implementation of the recommendation model.\nFor this article, we’ll explore one of them that I have found valuable in my work, and covers a wide variety of underlying algorithms for varied use-cases, known as `LightFM`.\nLightFM is a Python implementation of several popular recommendation algorithms for both implicit and explicit feedback types.\nImportantly, it allows you to incorporate both item and user metadata into the traditional matrix factorization algorithms, making it possible to generalize to new items (via item features) and new users (via user features).\nTo learn more about it, you can check its documentation [here](https://making.lyst.com/lightfm/docs/home.html).\n## Implementation\nThe complete code is available as a [Jupyter Notebook on GitHub](https://github.com/kapadias/mediumposts/blob/master/recommender/published_notebooks/recommendation_python_lightfm.ipynb).\n * **Loading data**\n * **Data inspection and preparation**\n * **Data preprocessing**\n * **Model training**\n * **Top n-recommendations**\n\n\n## Loading data\nFor this tutorial, we’ll use book reviews data by Goodreads. The datasets were collected in late 2017 from goodreads.com, where the data from users’ public shelves was scraped, i.e., everyone can see it on the web without login. User IDs and review IDs are anonymized.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/goodreads-1024x220.png)\nThe dataset is collected for academic use only. Please do not redistribute them or use for commercial purposes.\nThere are three groups of datasets:\n * meta-data of the books\n * user-book interactions\n * users’ detailed book reviews\n\n\nThese datasets can be merged together by matching book/user/review ids. For this tutorial, we’ll be using only the former two.\nYou can download the dataset we'reusing in this article from here:\n * Books Metadata: \n * User-Book Interactions: \n\n\npython\n\n```\n%%time\nbooks_metadata = pd.read_json('./data/goodreads_books_poetry.json', lines=True)\ninteractions = pd.read_json('./data/goodreads_interactions_poetry.json', lines=True)\n```\n\n## Data inspection & preparation\n### Books metadata\nLet’s start by inspecting the books’ metadata information. To develop a reliable and robust ML model, it is essential to get a thorough understanding of the available data.\nAs the first step, let’s take a look at all the available fields, and sample data:\npython\n\n```\nbooks_metadata.sample(2)\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM-4-1024x158.png)\nBooks Metadata (Sample)\nWhile all the available information is vital to extract contextual information to be able to train a better recommendation system, for this example, we’ll only focus on the selected fields that require minimal manipulation.\npython\n\n```\n# Limit the books metadata to selected fields\nbooks_metadata_selected = books_metadata[['book_id', \n'average_rating', 'is_ebook', 'num_pages', 'publication_year', \n'ratings_count', 'language_code']]\nbooks_metadata_selected.sample(5)\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM-5-1024x289.png)\nFiltered Books Metadata (Sample)\nNow that we have the data with selected fields, next, we’ll run it through pandas profiler to perform preliminary exploratory data analysis to help us better understand the available data.\npython\n\n```\nimport pandas_profiling\nimport numpy as np\n# replace blank cells with NaN\nbooks_metadata_selected.replace('', np.nan, inplace=True)\n# not taking book_id into the profiler report\nprofile = \\\npandas_profiling.ProfileReport(books_metadata_selected[['average_rating', \n'is_ebook', 'num_pages', 'publication_year', 'ratings_count']])\nprofile.to_file('./results/profiler_books_metadata_1.html')\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM-6-1024x490.png)\nConsidering the results from the profiler, we’ll perform following transformations to the dataset:\n * Replace the missing value of categorical values with another value to create a new category\n * Convert bin values for numeric variables into discrete intervals\n\n\npython\n\n```\n# using pandas cut method to convert fields into discrete intervals\nbooks_metadata_selected['num_pages'].replace(np.nan, -1, inplace=True)\nbooks_metadata_selected['num_pages'] = pd.to_numeric(books_metadata_selected['num_pages'])\nbooks_metadata_selected['num_pages'] = pd.cut(books_metadata_selected['num_pages'], bins=25)\n# rounding ratings to neares .5 score\nbooks_metadata_selected['average_rating'] = books_metadata_selected['average_rating'].apply(lambda x: round(x*2)/2)\n# using pandas qcut method to convert fields into quantile-based discrete intervals\nbooks_metadata_selected['ratings_count'] = pd.qcut(books_metadata_selected['ratings_count'], 25)\n# replacing missing values to year 2100\nbooks_metadata_selected['publication_year'].replace(np.nan, 2100, inplace=True)\n# replacing missing values to 'unknown'\nbooks_metadata_selected['language_code'].replace(np.nan, 'unknown', inplace=True)\n# convert is_ebook column into 1/0 where true=1 and false=0\nbooks_metadata_selected['is_ebook'] = books_metadata_selected.is_ebook.map(\n lambda x: 1.0*(x == 'true'))\nprofile = pandas_profiling.ProfileReport(books_metadata_selected[['average_rating', 'is_ebook', 'num_pages', \n 'publication_year', 'ratings_count']])\nprofile.to_file('./results/profiler_books_metadata_2.html')\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM-7-1024x490.png)\n### Interactions data\nAs the first step, let’s take a look at all the available fields and sample data:\npython\n\n```\ninteractions.sample(5)\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM-11-1024x160.png)\nLimit the data to only the selected fields:\npython\n\n```\n# Limit the books metadata to selected fields\ninteractions_selected = interactions[['user_id', 'book_id', 'is_read', 'rating']]\n# mapping boolean to string\nbooleanDictionary = {True: 'true', False: 'false'}\ninteractions_selected['is_read'] = interactions_selected['is_read'].replace(booleanDictionary)\nprofile = pandas_profiling.ProfileReport(interactions_selected[['is_read', 'rating']])\nprofile.to_file('./results/profiler_interactions.html')\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM-8-1024x413.png)\nConsidering the results from the profiler, we’ll convert is_read column to 1/0:\npython\n\n```\n# convert is_read column into 1/0 where true=1 and false=0\ninteractions_selected['is_read'] = interactions_selected.is_read.map(\n lambda x: 1.0*(x == 'true'))\n```\n\nSince we have two fields denoting interaction between a user and a book, `is_read` and `rating` — let’s see how many data points we have where the user hasn’t read the book but have given the ratings.\npython\n\n```\ninteractions_selected.groupby(['rating', 'is_read']).size().reset_index().pivot(columns='rating', index='is_read', values=0)\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM-9.png)\nFrom the above results, we can conclusively infer that users with ratings >= 1 have all read the book. Therefore, we’ll use the `ratings` as the final score, drop interactions where `is_read` is false, and limit interactions from random 5000 users to limit the data size for further analysis.\n## Data preprocessing\nNow, let’s transform the available data into CSR sparse matrix that can be used for matrix operations. We will start by the process by creating books_metadata matrix which is np.float64 csr_matrix of shape ([n_books, n_books_features]) — Each row contains that book’s weights over features. However, before we create a sparse matrix, we’ll first create an item dictionary for future reference.\npython\n\n```\nitem_dict ={}\ndf = books_metadata[['book_id', 'title']].sort_values('book_id').reset_index()\nfor i in range(df.shape[0]):\n item_dict[(df.loc[i,'book_id'])] = df.loc[i,'title']\n# dummify categorical features\nbooks_metadata_selected_transformed = pd.get_dummies(books_metadata_selected, columns = ['average_rating', 'is_ebook', 'num_pages', 'publication_year', 'ratings_count', 'language_code'])\nbooks_metadata_selected_transformed = books_metadata_selected_transformed.sort_values('book_id').reset_index().drop('index', axis=1)\nbooks_metadata_selected_transformed.head(5)\n# convert to csr matrix\nbooks_metadata_csr = csr_matrix(books_metadata_selected_transformed.drop('book_id', axis=1).values)\n```\n\nNext, we’ll create an iterations matrix which is np.float64 csr_matrix of shape ([n_users, n_books]). We’ll also create a user dictionary for future use cases.\npython\n\n```\nuser_book_interaction = pd.pivot_table(interactions_selected, index='user_id', columns='book_id', values='rating')\n# fill missing values with 0\nuser_book_interaction = user_book_interaction.fillna(0)\nuser_id = list(user_book_interaction.index)\nuser_dict = {}\ncounter = 0 \nfor i in user_id:\n user_dict[i] = counter\n counter += 1\n# convert to csr matrix\nuser_book_interaction_csr = csr_matrix(user_book_interaction.values)\nuser_book_interaction_csr\n```\n\n## Model training\nIdeally, we would build, train, and evaluate several models for our recommender system to determine which model holds the most promise for further optimization (hyper-parameter tuning).\nHowever, for this tutorial, we’ll train the base model with randomly selected input parameters for demonstrations.\npython\n\n```\nmodel = LightFM(loss='warp',\n random_state=2016,\n learning_rate=0.90,\n no_components=150,\n user_alpha=0.000005)\nmodel = model.fit(user_book_interaction_csr,\n epochs=100,\n num_threads=16, verbose=False)\n```\n\n## Top n-recommendations\nFor getting back the top-n recommendations, we’ll re-purpose the code by [Aayush Agrawal](https://github.com/aayushmnit) [here](https://github.com/aayushmnit/cookbook/blob/master/recsys.py).\npython\n\n```\ndef sample_recommendation_user(model, interactions, user_id, user_dict, \n item_dict,threshold = 0,nrec_items = 5, show = True):\n \n n_users, n_items = interactions.shape\n user_x = user_dict[user_id]\n scores = pd.Series(model.predict(user_x,np.arange(n_items), item_features=books_metadata_csr))\n scores.index = interactions.columns\n scores = list(pd.Series(scores.sort_values(ascending=False).index))\n \n known_items = list(pd.Series(interactions.loc[user_id,:] \\\n [interactions.loc[user_id,:] > threshold].index).sort_values(ascending=False))\n \n scores = [x for x in scores if x not in known_items]\n return_score_list = scores[0:nrec_items]\n known_items = list(pd.Series(known_items).apply(lambda x: item_dict[x]))\n scores = list(pd.Series(return_score_list).apply(lambda x: item_dict[x]))\n if show == True:\n print (\"User: \" + str(user_id))\n print(\"Known Likes:\")\n counter = 1\n for i in known_items:\n print(str(counter) + '- ' + i)\n counter+=1\nprint(\"\\n Recommended Items:\")\n counter = 1\n for i in scores:\n print(str(counter) + '- ' + i)\n counter+=1\n```\n\nLet’s check the results:\npython\n\n```\nsample_recommendation_user(model, user_book_interaction, 'ff52b7331f2ccab0582678644fed9d85', user_dict, item_dict)\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/LightFM-10.png)\n## Closing notes\nMachine learning has become increasingly popular over the past decade, and recent advances in computational availability have led to exponential growth to people looking for ways how new methods can be incorporated to advance the field of Recommendation Systems.\nOften, we treat the Recommendation System as black-box algorithms. Still, hopefully, this post addressed to shed light on the underlying math, and intuitions behind it, and high-level code to get you started with building your first recommendation model.\nIn the next article, we’ll go one step deeper into understanding how you can evaluate the performance of these models, and tune its hyper-parameters to get more intuitive and reliable results.\n## References\n[1] \n[2] Agarwal, Deepak K.. Statistical Methods for Recommender Systems. Cambridge University Press. Kindle Edition\n[3] Charu, A. (1997). Recommender systems The Textbook. In _Communications of the ACM_ (Vol. 40)\n[4] Mengting Wan, Julian McAuley, “[Item Recommendation on Monotonic Behavior Chains](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMengtingWan%2Fmengtingwan.github.io%2Fraw%2Fmaster%2Fpaper%2Frecsys18_mwan.pdf&sa=D&sntz=1&usg=AFQjCNGGcNRW1tSZKPWO0yZsr8mj7MkWuw)”, in _RecSys’18_\n[5] Mengting Wan, Rishabh Misra, Ndapa Nakashole, Julian McAuley, “[Fine-Grained Spoiler Detection from Large-Scale Review Corpora](https://www.google.com/url?q=https%3A%2F%2Fwww.aclweb.org%2Fanthology%2FP19-1248&sa=D&sntz=1&usg=AFQjCNG8xlMi09lyuzzMI8lCW58wrBEGsQ)”, in _ACL’19_\n[6] \nThanks for reading. _If you have any feedback, please feel to reach out by commenting on this post, messaging me on_[ _LinkedIn_](https://www.linkedin.com/in/shashankkapadia/) _, or shooting me an email (shmkapadia[at]gmail.com)_\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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