Recommender | C library for product recommendations | Recommender System library

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.)

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:

https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.KeyedVectors.add_vector

https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.KeyedVectors.add_vectors

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: model.dv.save(DOC_VECS_FILENAME)) & reloading them into a separate KeyedVectors (eg: growing_docvecs = KeyedVectors.load(DOC_VECS_FILENAME)).

Linking a library of an executable in CMake is achieved by using the target_link_libraries function. In your case,

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

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

Replace the following 2 lines:

I believe you should implement the required logic in the UserList class, with some sort of conditional rendering. Maybe something like:

Look at try-except and continue:

TensorFlow Lite is mainly for inference use cases. After training TF models in the desktop/server side, you can convert the trained TF model to the corresponding TensorFlow Lite model to deploy them to mobile with some techniques, for example, quantizations.

Thanks all.

I was using an older Kafka version.

I upgraded Kafka in the cluster from kafka_2.12-1.1.0 to the latest stable version kafka_2.12-2.7.0. I also upgraded zookeeper from zookeeper-3.4.6 to apache-zookeeper-3.6.2-bin version.

In addition in the run file I added the following: