Modeling Individual Users' Responsiveness to Maximize Recommendation Impact

Recommender systems provide personalized information based on a user's preferences. Differences in preferences among users are estimated from past records such as click logs or purchase logs. Recommender systems typically assume that users will respond to recommendations, provided that their favorite items are correctly selected. However, the responsiveness to recommendations depends on the type of users; while some users might be easily persuaded to take action, others might be more hesitant. In this paper, we propose a purchase prediction model that incorporates the differences in the responsiveness. Improvement in purchase prediction and recommendation impact, which is defined as the increase in purchase probability through recommendations, was verified using a grocery shopping dataset. These results demonstrate the importance of modeling the responsiveness of individual users. In cases where recommendation logs are insufficient, the responsiveness needs to be estimated from other sources. Consequently, we investigated the correlation of the responsiveness with user attributes and item attributes. The recommendation impact of the model estimated from the correlated attributes was almost comparable to that of the model estimated from recommendation logs. These findings can help overcome the cold-start problem of inadequate recommendation logs. Our study presents a new direction in the field of personalization based on the responsiveness to recommendations.


  • Masahiro Sato
    Communication Technology, Research & Technology Group
  • Hidetaka Izumo
    Fuji Xerox Information Systems Co., Ltd.
  • Takashi Sonoda
    Communication Technology, Research & Technology Group