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Avoiding monotony: improving the diversity of recommendation lists

Published: 23 October 2008 Publication History

Abstract

The primary premise upon which top-N recommender systems operate is that similar users are likely to have similar tastes with regard to their product choices. For this reason, recommender algorithms depend deeply on similarity metrics to build the recommendation lists for end-users.
However, it has been noted that the products offered on recommendation lists are often too similar to each other and attention has been paid towards the goal of improving diversity to avoid monotonous recommendations.
Noting that the retrieval of a set of items matching a user query is a common problem across many applications of information retrieval, we model the competing goals of maximizing the diversity of the retrieved list while maintaining adequate similarity to the user query as a binary optimization problem. We explore a solution strategy to this optimization problem by relaxing it to a trust-region problem.This leads to a parameterized eigenvalue problem whose solution is finally quantized to the required binary solution. We apply this approach to the top-N prediction problem, evaluate the system performance on the Movielens dataset and compare it with a standard item-based top-N algorithm. A new evaluation metric ItemNovelty is proposed in this work. Improvements on both diversity and accuracy are obtained compared to the benchmark algorithm.

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    cover image ACM Conferences
    RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
    October 2008
    348 pages
    ISBN:9781605580937
    DOI:10.1145/1454008
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    Publication History

    Published: 23 October 2008

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    Author Tags

    1. accuracy
    2. diversity
    3. metrics
    4. novelty
    5. recommender system

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    RecSys08: ACM Conference on Recommender Systems
    October 23 - 25, 2008
    Lausanne, Switzerland

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    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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    • (2025)A generative and discriminative model for diversity-promoting recommendationInformation Systems10.1016/j.is.2024.102488128(102488)Online publication date: Feb-2025
    • (2025)Pareto selective error feedback suppression for popularity–diversity balanced session-based recommendationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109911142(109911)Online publication date: Feb-2025
    • (2024)Analyzing the Impact of Information Features on User Continuance Intent in Recommendation SystemsInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.35390520:1(1-36)Online publication date: 17-Sep-2024
    • (2024)Deep Reinforcement Learning for Boosting Individual and Aggregate Diversity in Product Recommendation SystemsProceeding of the 2024 5th International Conference on Computer Science and Management Technology10.1145/3708036.3708043(39-48)Online publication date: 18-Oct-2024
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