Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations
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Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations Shameem A. Puthiya Parambath1 · Sanjay Chawla1 Received: 7 November 2019 / Accepted: 25 July 2020 © The Author(s) 2020
Abstract Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to be item cold-start recommendation: how to effectively recommend items with no observed or past preference information. Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem. The crux of our approach lies in representing the items as soft-cluster embeddings in the space spanned by the side-information associated with the items. Though many item embedding approaches have been proposed for item cold-start recommendations in the past—and simple as they might appear—to the best of our knowledge, the approach based on soft-cluster embeddings has not been proposed in the research literature. Our experimental results on four benchmark datasets conclusively demonstrate that the proposed algorithm makes accurate recommendations in item cold-start settings compared to the state-of-the-art algorithms according to commonly used ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The performance of our proposed algorithm on the MovieLens 20M dataset clearly demonstrates the scalability aspect of our algorithm compared to other popular algorithms. We also propose the metric Cold Items Precision (CIP) to quantify the ability of a system to recommend cold-start items. CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm.
Responsible editor: Ira Assent, Carlotta Domeniconi, Aristides Gionis, Eyke Hüllermeier.
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Shameem A. Puthiya Parambath [email protected] Sanjay Chawla [email protected]
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Qatar Computing Research Institute, HBKU Research Complex, Doha, Qatar
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S. A. Puthiya Parambath, S. Chawla
Keywords Recommender systems · Item recommendation · Item cold-start problem · Soft-cluster embeddings
1 Introduction Personalized recommender systems assist users in exploring large collections of items efficiently to deal with the problem of information overload by filtering the items into small selections tailored to an individual’s personal preference. This is achieved by inferring the users’ intrinsic preferences for different items. In typical use cases, items can be simple tweet messages, food recipes, electronic gadgets or vehicles. Two popular approaches for recommendation are: (i) content based and (ii) collaborative. Content-based systems recommend items which are similar in content to the ones a user favoured in the past whereas collaborative systems recommend items that users with similar tastes favoured
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