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UBC Theses and Dissertations

Revisiting recommendations: from customers to manufacturers Agarwal, Shailendra

Abstract

Recommender systems exploit user feedback over items they have experienced for making recommendations of other items that are most likely to appeal to them. However, users and items are but two of the three types of entities participating in this ecosystem of recommender systems. The third type of entities are the manufacturers of the products, and users are really their customers. Traditional recommender systems research ignores the role of this third entity type and exclusively focuses on the other two. What might item producers bring to recommender systems research? Their objectives are related to their business and are captured by questions such as “what kind of (new) products should I manufacture that will maximize their popularity?” These questions are not asked in a vacuum: manufacturers have constraints, e.g., a budget. The idea is that the user feedback data (e.g., ratings) capture users’ preferences. The question is whether we can learn enough intelligence from it, so as to recommend new products to manufacturers that will help meet their business objectives. We propose the novel problem of new product recommendation for manufacturers. We collect real data by crawling popular e-commerce websites, and model cost and popularity as a function of product attributes and their values. We incorporate cost constraints into our problem formulation: the cost of the new products should fall within the desired range while maximizing the popularity. We show that the above problem is NP-hard and develop a pseudo-polynomial time algorithm for the recommendations generation. Finally, we conduct a comprehensive experimental analysis where we compare our algorithm with several natural heuristics on three real data sets and perform scalability experiments on a synthetic data set.

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License

Attribution 2.5 Canada

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