Query2vec: Search query expansion with query embeddings
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Further restaurants will be miscategorized or eg have the word "sushi" in the name, and yet not appear in the sushi category (not sure if there's actually a sushi category in the app, just using that as an example).
If I’m looking for a dish or cuisine, why not display it in a visual fashion like Uber eats? There’s usually not that many options to actually sift through.
For example the first graphic of the embedding space has a very clear mixture of restaurant names and food items. Feels a bit messy.
If a primitive embedding unit is a search query instead of a word, then I assume that a query vector model is trained on a limited dictionary of queries. I wonder if that implies that the trained vector model can encode only search queries that are already present in its dictionary? If not, I think it would be interesting to know more about how the closed dictionary problem was solved.
One of the most common “off the shelf” search solutions is to train an embedding for X (where X is whatever you want to search) using some implicit similarity or triplet loss for examples that have natural positive labels (like due to proximity in the word2vec case) plus random negative sampling, and use the embedding in an exact or approximate nearest neighbor index.
In fact, it’s even very “off the shelf” to use Siamese networks for multi-modal embeddings, for example simultaneously learning embeddings that put semantically similar queries and food images into the same ANN vector space.
I think the blog post is very cool don’t get me wrong, but no part of this is novel (if that’s what you were going for, I can’t tell). This exact end to end pipeline has been used for image search, collaborative filtering (customer & product embeddings), various recommender systems where the unit of embedding is something like “pages” or “products” or “blog posts” or other product primitives for whatever type of business, in production across several different companies I’ve worked for.
In the IR community you will see image2image search implemented by passing an image through a headless CNN and then do ANN search on the embedding space. Then that can lead to discussion about cross-model hashing which you suggested. I think also you touched on LTR techniques w/ siamese networks. For example we can use a siamese network to learn the diff between two embeddings.
In the Recys community for collaborative filtering you can take interaction data between users and items and decompose that matrix into two parts using SVD which is called the user and item embeddings. Also in the recsys community you see people doing item2vec (what I did) for the item embeddings and then aggregating those for the user representation (embeddings).
Maybe you will grant me the novelty of this method in making the connection between queries and converted restaurants as a similarity metric and then applying it to the IR use case of search query expansion? Should the post be more clear that I am not claiming to have invented item2vec or ANN (approximate nearest neighboors)?
The “trick” is to find some naturally occurring property that indicates a pair of examples has the positive label, such as two users selecting a given product, two text queries leading to clicking on the same image, two different blog posts with the same keyword, etc. This combined with an efficient way to sample acceptable negative pairs that do not express the trait that indicates positive label.
That + deciding how to approach the loss function (e.g. cosine similarity, euclidean distance, distance in a hash space, triplet loss, should it have a margin) is the whole trick of the problem.
The black box that produces embeddings (DNN, doc2vec, sparse vector from tfidf-like features, whatever) and the nearest neighbor piece are standard plug and play.
The high-level concept of a product is important for any commerce company w/ an unbounded and unstructured product catalog. In this case, the concept of a Dish is a very valuable primitive for Caviar and GH to understand.
You propose an interesting technique for dish tagging:
1. train word2vec on menu item text 2. Generate menu item embeddings by aggregating respective word vectors 3. Curate dish (label) set 4. Generate embeddings for dish labels by aggregating word vectors 5. Tag menu items w/ their dish label by NN search.
Then once you have this high-level concept of dish you can drive all kinds of interesting product innovations:
* trending asian noodle dishes * tacos in san Diego * It also helps w/ the item sparsity problem in matrix factorization
Do I understand?
I'm going to try this on our next learning Friday.
We have taken a different route for dish tagging. First we have a graphical model tag some items w/ low coverage but high precision and then we feed that into a classifier to generalize and provider full coverage. It's like a generative model fed into a discriminative model.
Newbie NLP dev here. What's this concept of coverage vs precision that you speak of? It sounds intriguing.
But to answer you question here: For production ANN we have Annoy integrated into our backend as a service. Annoy was an easy choice bc it checked out box for JVM support.
For training the models we have endless amounts of behavioral data, so we didn't even need to look at transfer learning. For this query2vec example, it was trained on 1 year of queries which takes 15min/epoch on an AWS p2 GPU. We do all our preprocessing (heavy normalization) in pyspark.
I'm trying to effect change by making meaningful contributions to our search pipeline by applying novel techniques from information retrieval literature.
For example, with this query expansion technique, we can service search queries in small markets (not a lot of restaurants) more completely by showing something instead of nothing. Or in big markets, we can service obscure, tail queries by fining something similar. This helps increase the overall recall of the search system, making it better!
Don't loose faith in GH yet!