Building-Recommendation-Engines | Building Recommendation Engines by Packt | Recommender System library

 by   PacktPublishing Java Version: Current License: MIT

kandi X-RAY | Building-Recommendation-Engines Summary

kandi X-RAY | Building-Recommendation-Engines Summary

Building-Recommendation-Engines is a Java library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Recommender System applications. Building-Recommendation-Engines has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Building-Recommendation-Engines build file is not available. You can download it from GitHub.

A recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general. ##Instructions and Navigations All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

            kandi-support Support

              Building-Recommendation-Engines has a low active ecosystem.
              It has 28 star(s) with 16 fork(s). There are 4 watchers for this library.
              It had no major release in the last 6 months.
              There are 0 open issues and 1 have been closed. On average issues are closed in 1121 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Building-Recommendation-Engines is current.

            kandi-Quality Quality

              Building-Recommendation-Engines has 0 bugs and 16 code smells.

            kandi-Security Security

              Building-Recommendation-Engines has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Building-Recommendation-Engines code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              Building-Recommendation-Engines is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              Building-Recommendation-Engines releases are not available. You will need to build from source code and install.
              Building-Recommendation-Engines has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              Building-Recommendation-Engines saves you 100 person hours of effort in developing the same functionality from scratch.
              It has 255 lines of code, 9 functions and 9 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Building-Recommendation-Engines and discovered the below as its top functions. This is intended to give you an instant insight into Building-Recommendation-Engines implemented functionality, and help decide if they suit your requirements.
            • Main entry point
            • Runs the average difference
            • Display recommendations
            • Display recommender
            • Demonstrates the recommenders
            Get all kandi verified functions for this library.

            Building-Recommendation-Engines Key Features

            No Key Features are available at this moment for Building-Recommendation-Engines.

            Building-Recommendation-Engines Examples and Code Snippets

            No Code Snippets are available at this moment for Building-Recommendation-Engines.

            Community Discussions


            Dataframe users who did not purchase item for user-item collaborative filtering
            Asked 2022-Mar-05 at 12:35

            I intend to use a hybrid user-item collaborative filtering to build a Top-N recommender system with TensorFlow Keras

            currently my dataframe consist of |user_id|article_id|purchase

            purchase is always TRUE because the dataset is a history of user - article purchases

            This dataset has 800,000 rows and 3 columns

            2 Questions
            1. How do I process it such that I will have 20% purchase = true and 80% purchase = false to train the model?

            2. Is a 20%, 80% true:false ratio good for this use case?



            Answered 2022-Mar-05 at 12:35
            1. How do I process it such that I will have 20% purchase = true and 80% purchase = false to train the model?

            Since you only have True values, it means that you'll have to generate the False values. The only False that you know of are the user-item interactions that are not present in your table. If your known interactions can be represented as a sparse matrix (meaning, a low percentage of the possible interactions, N_ITEMS x N_USER, is present) then you can do this:

            1. Generate a random user-item combination
            2. If the user-item interaction exists, means is True, then repeat step 1.
            3. If the user-item interaction does not exist, you can consider it a False interaction.

            Now, to complete your 20%/80% part, just define the size N of the sample that you'll take from your ground truth data (True values) and take 4*N False values using the previous steps. Remember to keep some ground truth values for your test and evaluation steps.

            1. Is a 20%, 80% true:false ratio good for this use case?

            In this case, since you only have True values in your ground truth dataset, I think the best you can do is to try out different ratios. Your real world data only contains True values, but you could also generate all of the False values. The important part to consider is that some of the values that you'll consider False while training might actually be True values in your test and validation data. Just don't use all of your ground truth data, and don't generate an important portion of the possible combinations.

            I think a good start could be 50/50, then try 60/40 and so on. Evaluate using multiple metrics, see how are they changing according to the proportion of True/False values (some proportions might be better to reach higher true positive rates, other will perform worse, etc). In the end, you'll have to select one model and one training procedure according to the metrics that matter the most to you.



            How to Deploy ML Recommender System on AWS
            Asked 2021-Nov-05 at 01:24

            I'm dabbling with ML and was able to take a tutorial and get it to work for my needs. It's a simple recommender system using TfidfVectorizer and linear_kernel. I run into a problem with how I go about deploying it through Sagemaker with an end point.



            Answered 2021-Nov-05 at 01:24

            I came to the conclusion that I didn't need to deploy this through SageMaker. Since the final linear_kernel output was a Dictionary I could do quick ID lookups to find correlations.

            I have it working on AWS with API Gateway/Lambda, DynamoDB and an EC2 server to collect, process and plug the data into DynamoDB for fast lookups. No expensive SageMaker endpoint needed.



            What does .nonzero()[0] mean when we want to compute the sparsity of a matrix?
            Asked 2021-Oct-18 at 17:43

            I am trying to learn about recommender systems in Python by reading a blog that contains a great example of creating a recommender system of repositories in GitHub.

            Once the dataset is loaded with read_csv(), the person that wrote the code decided to convert that data into a pivot_table pandas for visualizing the data in a more simple way. Here, I left you an image of that part of the code for simplicity:

            enter image description here

            In that table, rows are the users and columns are the repositories. The cross section between a row and a column is the punctuation that a user gives to a particular repository.

            Due to the fact that many of the elements of that table are null (we can say that we are having a sparse matrix, very typical in machine learning), he decided to study the level of sparsity of the matrix by means of this code:



            Answered 2021-Oct-18 at 17:43

            By default, nonzero will return a tuple of the form (row_idxs, col_idxs). If you hand it a one-dimensional array (like a pandas series), then it will still return a tuple, (row_idxs,). To access this first array, we still must index ratings.nonzero()[0] to get the first-dimension index of nonzero elements.

            More info available on the numpy page for nonzero here, as both pandas and numpy use the same implementation.



            how to make an integer index corresponding to a string value?
            Asked 2021-Jul-25 at 05:41

            I'm currently building a recommender system using Goodreads data.

            I want to change string user ids into integers. Current user ids are like this: '0d688fe079530ee1fe6fa85eab10ec5c'

            I want to change it into integers(e.g. 1, 2, 3, ...), to have the same integer ids which share the same string ids. I've considered using function df.groupby('user_id'), but I couldn't figure out how to do this.

            I would be very thankful if anybody let me know how to change.



            Answered 2021-Jul-25 at 04:52

            Use pd.factorize as suggested by @AsishM.

            Input data:



            How can I ensure that all users and all items appear in the training set of my recommender system?
            Asked 2021-Jun-11 at 20:37

            I am building a recommender system in Python using the MovieLens dataset ( In order for my system to work correctly, I need all the users and all the items to appear in the training set. However, I have not found a way to do that yet. I tried using sklearn.model_selection.train_test_split on the partition of the dataset relevant to each user and then concatenated the results, thus succeeding in creating training and test datasets that contain at least one rating given by each user. What I need now is to find a way to create training and test datasets that also contain at least one rating for each movie.



            Answered 2021-Jun-11 at 20:37

            This requirement is quite reasonable, but is not supported by the data ingestion routines for any framework I know. Most training paradigms presume that your data set is populated sufficiently that there is a negligible chance of missing any one input or output.

            Since you need to guarantee this, you need to switch to an algorithmic solution, rather than a probabilistic one. I suggest that you tag each observation with the input and output, and then apply the "set coverage problem" to the data set.

            You can continue with as many distinct covering sets as needed to populate your training set (which I recommend). Alternately, you can set a lower threshold of requirement -- say get three sets of total coverage -- and then revert to random methods for the remainder.



            LensKit Recommender only returns results for some users, otherwise returns empty DataFrame. Why is this happening?
            Asked 2021-May-23 at 02:53

            I am trying to implement a group recommender system with the Django framework, using the LensKit tools for Python (specifically a Recommender object which adapts the UserUser algorithm). However, it only returns individual recommendations in some cases (for some specific users), but it always returns recommendations for groups of users (I create a hybrid user whose scores are the average of group members' scores and request recommendations for it). Below is my implementation for requesting recommendations for an individual user, as well as for a group:



            Answered 2021-May-23 at 02:53

            The most likely cause of this problem is that the user-user recommender cannot build enough viable neighborhoods to provide recommendations. This is a downside to neighborhood-based recommendations.

            The solutions are to either switch to an algorithm that can always recommend for a user with some ratings (e.g. one of the matrix factorization algorithms), and/or use a fallback algorithm such as Popular to recommend when the personalized collaborative filter cannot recommend.

            (Another solution would be to implement one of the various cold-start recommenders or a content-based recommender for LensKit, but none are currently provided by the project.)



            How to get similarity score for unseen documents using Gensim Doc2Vec model?
            Asked 2021-May-19 at 09:07

            I have trained a gensim doc2vec model for an English news recommender system. the model was trained with 40K news data. I am using the code below to recommend the top 5 most similar news for e.g. news_1:



            Answered 2021-May-19 at 09:07

            There's a bulk contiguous vector structure initially created by training, for the initial known set of vectors. It's amenable to the every-candidate bulk vector calculation at the heart of most_similar() - so that operation goes about as fast as it can, with the right vector libraries for your OS/processor.

            But, that structure wasn't originally designed with incremental expansion in mind. Indeed, if you have 1 million vectors in a dense array, then want to add 1 to the end, the straightforward approach requires you to allocate a new 1-million-and-1 long array, bulk copy over the 1 million, then add the last 1. That works, but what seems like a "tiny" operation then takes a while, and ever-longer as the structure grows. And, each add more-than-doubles the temporary memory usage, for the bulk copy. So, the naive pattern of adding a whole bunch of new items individuall in a loop can be really slow & memory-intensive.

            So, Gensim hasn't yet focused on providing a set-of-vectors that's easy & efficient to incrementally grow with new vectors. But, it's still indirectly possible, if you understand the caveats.

            Especially in gensim-4.0.0 & above, the .dv set of doc-vectors is an instance of KeyedVectors with all that class's standard functions. Thos include the add_vector() and add_vectors() methods:



            You can try these methods to add your new inferred vectors to the model.dv object - and then they'll also be ncluded in folloup most_similar() results.

            But keep in mind:

            1. The above caveats about performance & memory-usage - which may be minor concerns as long as your dataset isn't too large, or manageable if you do additions in occasional larger batches.

            2. The containing Doc2Vec model generally isn't expecting its internal .dv to be arbitrarily modified or expanded by other code. So, once you start doing that, parts of the model may not behave as expected. If you have problems with this, you could consider saving-aside the full Doc2Vec model before any direct-tampering with its .dv, and/or only expanding a completely separate instance of the doc-vectors, for example by saving them aside (eg: & reloading them into a separate KeyedVectors (eg: growing_docvecs = KeyedVectors.load(DOC_VECS_FILENAME)).



            Unable to create dataframe from RDD
            Asked 2021-May-10 at 14:34

            I am trying to create a recommender system from this kaggle dataset: f7a1f242-c


            the file is called: "user_artist_data_small.txt"

            The data looks like this:

            1059637 1000010 238

            1059637 1000049 1

            1059637 1000056 1

            1059637 1000062 11

            1059637 1000094 1

            I'm getting an error on the third last line of code.



            Answered 2021-May-10 at 14:09

            Just create a dataframe using the CSV reader (with a space delimiter) instead of creating an RDD:



            Combining output in pandas?
            Asked 2021-Apr-07 at 23:00

            I have a movie recommender system I have been working on and currently it is printing two different sets of output because I have two different types of recommendation engines. Code is like this:



            Answered 2021-Apr-07 at 23:00

            If the return type of get_input_movie() is a Pandas DataFrame or a Pandas Series, you can try:

            Replace the following 2 lines:



            How to get a while loop to start over after error?
            Asked 2021-Mar-23 at 21:34

            would like to say I still feel fairly new too python in general. But I have a movie recommender system that I have been working on, and the way I have it setup is for the user to enter a movie in the console and then it spits out 10 recommendations and ask for another movie. When a misspelled movie is entered, it gives error message KeyError: 'Goodfellas' and it stops running. I would like for it to just start the loop over, until the user ends the loop using my break word. Here is my code for reference.



            Answered 2021-Mar-23 at 21:19

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install Building-Recommendation-Engines

            You can download it from GitHub.
            You can use Building-Recommendation-Engines like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Building-Recommendation-Engines component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer For Gradle installation, please refer .


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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