| Popis: |
On many online platforms, more items are being added every day, requiring users to select efficiently the desired item from among these candidates. In similar environments, recommender systems have been introduced, allowing users to select from among recommended items. While the immediate advantages of recommender systems, such as presenting users with pertinent information efficiently and boosting page views, are well recognized, concerns have been raised that these systems might constrict users’ choices and amplify echo chambers. Despite these concerns, the detrimental impacts of recommender systems on user behavior have not been thoroughly investigated. To address the societal challenges stemming from the limitation in the breadth of information to which users are exposed, a novel recommender system offering a wider array of choices is essential. In this study, we examine the user experience of the Nikkei Electronic Version, a major news delivery service in Japan, from the perspective of diversity. Using the language of article titles, we evaluate the diversity of recommended results and click logs presented to users. In addition to an analysis of the actual service, we conduct user experiments with participants recruited from among users of the service to investigate the effect of recommendation algorithms specialized in increasing diversity, separate from the actual service. We propose a recommender system based on the concept of extracting articles as far apart as possible within the language space. Through static experiments and user experiments, we show that the proposed recommender system is effective in diversifying the recommendation list. Furthermore, we show that when candidate articles that are likely to be clicked can be properly extracted, the proposed recommender system is effective in diversifying the articles that users click. Our research is aimed at improving long-term satisfaction by recommending content that users can enjoy in the short term while simultaneously ensuring diversity. |
