Work

Attribute-aware Diversification for Sequential Recommendations

Year: 2020

Type: conference-paper

Abstract: Users prefer diverse recommendations over homogeneous ones. However, most previous work on Sequential Recommenders does not consider diversity, and strives for maximum accuracy, resulting in homogeneous recommendations. In this paper, we consider both accuracy and diversity by presenting an Attribute-aware Diversifying Sequential Recommender (ADSR). Specifically, ADSR utilizes available attribute information when modeling a user's sequential behavior to simultaneously learn the user's most likely item to interact with, and their preference of attributes. Then, ADSR diversifies the recommended items based on the predicted preference for certain attributes. Experiments on two benchmark datasets demonstrate that ADSR can effectively provide diverse recommendations while maintaining accuracy.

Source: UvA-DARE (University of Amsterdam)

DOI: 10.48550/arxiv.2008.00783

Authors: Steenvoorden, Anton, Di Gloria, Emanuele, Chen, Wanyu, Ren, Pengjie, de Rijke, Maarten

Institutions: Ahold Delhaize (Netherlands), University of Amsterdam, National University of Defense Technology

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3047090067

FWCI: 0.379

Cites: 9

Cited by: 2

Related to: 20