Vector Embedding Solution for Recommendation System (2024)

Abstract: We propose a vector embedding approach for recommendation systems aimed at identifying product affinities and suggesting complementary items. By capturing relationships between products, the model delivers highly relevant recommendations based on the context. A neural network is trained on purchase data to generate word embeddings, represented as a weight matrix. The resulting model predicts complementary products with top-20 and top-50 precision scores of 0.59251 and 0.29556, respectively. These embeddings effectively identify products likely to be co-purchased, enhancing the relevance and accuracy of the recommendations.

## introduction words can be represented as real-valued vectors in lower dimensional space, that encodes its meaning based on the context. the words that appear closer in the vector space are considered to have similar meaning. word2vec @xcite and glove @xcite are two most important word embeddings that captures the semantic similarity of words. the idea of word embeddings can be applied to commercial products as well, where the products are encoded in vector space. this aids the product recommendation system to identify and predict related products based on the purchase trends by customers. product embeddings, which captures the affinities between products, is very useful when applied to to information retrieval and recommender systems, as it enable customers to find and discover highly relevant products based on context, as the embeddings capture the semantic relation between the products, by learning their co-purchase pattern. given a list of products bought together in a single session, the products would best complement the products being considered, can be easily deduced when embedding is applied. the idea of dual embedding is implemented in @xcite for representing products which can be created from co-purchased data. this helps in recommending complementary products that can used in recommendations systems. the recommended model uses both input and output weight matrices for computing the similarity, which results in identifying the related words. the model also uses synthetic samples to augment the model so that it can handle the sparsity of purchase data. alternatives to a given product can be identified by extracting relationship between products based on their frequency of occurrence and co-purchase history. @xcite applies word2vec and fasttext models to obtain embeddings to help product recommendation. @xcite uses word2vec model to identify similarity between items purchased in a session and analyse the number of common recommendations of selected items, to obtain the products that can be purchased together. a model to recommend products to the customer on an e-commerce platform, based on the customer profile is implemented in @xcite . it uses the purchase history and product reviews, along with the copurchase patterns to create profiles for users and products. the interaction between products and users are identified using word embedding techniques. ## conclusion this paper addressed the challenge of representing the commercial products as vectors in a low dimensional space, with the primary objective of improving the recommendations in an e-commerce platform. the feed forward network designed gives a top 20 and top 50 precision of 0.59251 and 0.29556 when compared to the existing dual embedding model @xcite with precision values 0.0437 and 0.0322 respectively. the reason behind smaller value of precision for dual embedding may be due to the size of the training data and also because of the synthetic data generated using augmentation. the findings of this paper provide a practical solution for identifying the complementary products which aids the recommendation system. the product embeddings contributes to the understanding of product semantics, relationships, and user interactions by converting the information about products from each purchase session into a vector space. this enables more effective search, recommendation, analysis, and enrichment of product-related information. classic scent liquid dish soap as an example. the intuition behind this failure cases of embedding being that since the products in the same order are considered as related, there may be rare samples in the dataset where unrelated items are purchased in the same order. this can happen when the order size is too large. such failure cases can be considered as a research problem which will be addressed in future works. the model currently uses instacart dataset, and limited work is available using the same. we will perform the comparison of the model's performance with commonly used approaches, considering different datasets while extending this work, in future, so as to improve the generalization of the model.