Abstract
Recommender systems are emerging in e-commerce as important promotion tools to assist customers to discover potentially interesting items. Currently, most of these are single-objective and search for items that fit the overall preference of a particular user. In real applications, such as restaurant recommendations, however, users often have multiple objectives such as group preferences and restaurant ambiance. This paper highlights the need for multi-objective recommendations and provides a solution using hypergraph ranking. A general User–Item–Attribute–Context data model is proposed to summarize different information resources and high-order relationships for the construction of a multipartite hypergraph. This study develops an improved balanced hypergraph ranking method to rank different types of objects in hypergraph data. An overall framework is then proposed as a guideline for the implementation of multi-objective recommender systems. Empirical experiments are conducted with the dataset from a review site Yelp.com, and the outcomes demonstrate that the proposed model performs very well for multi-objective recommendations. The experiments also demonstrate that this framework is still compatible for traditional single-objective recommendations and can improve accuracy significantly. In conclusion, the proposed multi-objective recommendation framework is able to handle complex and changing demands for e-commerce customers.
Highlights
Today, the volume of data on an e-commerce site may become extremely large
This paper considers the changing user demands in different circumstances, which is partially related to existing context-aware recommender system (CARS) studies [2, 45]
The adopted Yelp dataset has no records about user preferences under different contexts, but it provides user comments, which can be treated as the extended context entities and user-context-item relationships defined in our UIAC data model
Summary
The volume of data on an e-commerce site may become extremely large. For example, Taobao.com announced that there are currently over 0.8 billion daily-active products on this C2C e-commerce site. With the proposed UIAC data model, we can decompose a multi-objective recommendation to requirements to multiple entities from user, item, attribute and context entities. The multipartite hypergraph is constructed from a wide range of relationships in an e-commerce application and is special in that the edge degrees (number of connected nodes) vary greatly from two (pairwise edges) to thousands (group edges) or even larger In this circumstance, we find that the traditional hypergraph ranking models suffer from a ranking bias problem. The last section concludes the findings of this study and suggests future research directions
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