Deep Learning-based Online Alternative Product Recommendations at Scale (2020)

Abstract: Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customers’ needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12% in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.

## introduction recommender systems are pervasive in ecommerce and other web systems @xcite . alternative product recommendation is an important way to help customers easily find the right products and speed up their buying decision process. for example, if a customer is viewing a "25.5 cu. ft. counter depth french door refrigerator in stainless steel", she may also be interested in other french door refrigerators in different brands but with similar features such as capacity, counter depth, material, etc. there are two main ways to obtain an alternative product list for a given product. first is a content-based recommendation approach. if two products have similar attributes or content so that one can be replaced by the other, we can consider them as alternative products. word2vec has been used to learn item embeddings for comparing item similarities (caselles-dupre, lesaint, and royo-letelier 2018). however, this unsupervised learning process does not guarantee the embedding distance is consistent with customers' shopping preference. the second way is to leverage customer behavior to find alternative products in the style of item-to-item collaborative filtering @xcite . if customers frequently consider two products together, one product can be recommended as an alternative for the other. unfortunately, this approach has a cold start problem. in this work, we formulate the recommendation problem into a supervised product embedding learning process. to be specific, we develop a deep learning based embedding approach using siamese network, which leverages both product content (including title and description) and customer behavior to generate top-n recommendations for an anchor product. our contributions are as follows: ## problem formulation we have the textual information 𝑇 = {𝑥 ! , … , 𝑥 " } (a concatenation of product title and description) of a catalog of products 𝑃 = {𝑝 ! , … , 𝑝 " } to make recommendations. the goal of the alternative recommendation is to learn a embedding projection function 𝑓 # so that the embedding of an anchor product that is viewed by a customer 𝑓 # (𝑥 @xmath0 ) and 𝑓 # (𝑥 % ) as the energy function. the problem is how to learn a function as the embedding projection function 𝑓 # to better capture the semantic meanings of the product textual information and project a sequence of tokens 𝑥 / into an embedding vector of size d. the total loss over the training data set 𝑋 = 0𝑥 $ (/) , 𝑥 % (/) , 𝑦 (/) 2 is given by the loss functions for the positive and negative cases are given by: based on the loss function, the problem is how to build a network that can learn part of the product information that is important for customers and project a product to the right embedding space that is consistent with customers' preference. ## deep learning embedding approach textual data and co-compared data product information: from the ecommerce site catalog data, we extract the product id, product title and description as the raw textual data with an example in ## performance evaluation in this section, we describe how we evaluate the effectiveness and efficiency of our deep learning model with offline evaluation and online a/b test. we use our production data to validate the results since this is a unique case for us. we did not find exact similar open data set with similar customer behaviors that can be used for our evaluation. ## related work the traditional method for recommender systems is content-based recommendations @xcite . this method can handle the cold start problem well. collaborative filtering is another method based on user behaviors. for example, matrix factorization @xcite ) is a widely used method for collaborative filtering. our two baseline algorithms, one is considered as content-based and the other is considered as collaborative filtering using user behavior data with the co-compared format. deep learning now has been widely used not only in the academic community, but also in industrial recommender system settings, such as airbnb's listing recommendations (grbovic and cheng 2018) and pinterest's recommendation engine (ying et al. 2018). most of recent deep learning papers (e.g., wang et al. 2019; ebesu, shen, and fang 2018) have been focused on sequential recommendations. (neculoiu et al. 2016) presents a deep network using siamese architecture with character-level bidirectional lstm for job title normalization. (mueller and thyagarajan 2016) also presents a siamese adaptation of the lstm to learn sentence embedding. however, this work needs human annotated labels while our labels are extracted from clickstream data. our work more focuses on providing alternative recommendations by learning product embedding from product textual data and customer signals. ## conclusion recommender systems are core functions for online retailers to increase their revenue. to help customers easily find alternative products in an automated way, we develop a deep learning approach to generate product embeddings based on a siamese network with bidirectional lstm. we extract co-compared data from customer clickstream and product textual data to train the network and generate the embedding space. our approach significantly improves the coverage of similar products as well as improving recall and precision. our algorithm also shows promising results on conversion rate in an online a/b test.