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Proactive Recommendation Delivery

Published: 07 September 2016 Publication History

Abstract

The main purpose of Recommender Systems is to minimize the effects of information/choice overload. Recommendations are usually prepared based on the estimation of what would be useful or interesting to users. Thus, it is important that they are relevant to users, whether to their information needs, current activity or emotional state. This requires deep understanding of users' context but also the knowledge of the history of previous users' interactions within the system (e.g. clicks, views, etc.). But even when the recommendations are highly relevant, their delivery to users can be very problematic. Many existing systems require active user participation (explicit interaction with the recommender system) and attention. Or, on other side of spectrum, there are RS that handle recommendation delivery without any consideration for users' preferences of when, where or how the recommendations are being delivered. Proactive Recommender Systems promise a more autonomous approach for recommendation delivery, by anticipating information needs in advance and acting on users' behalf with minimal efforts and without disturbance. This paper describes our work and interest in identifying and analyzing the factors that can influence acceptance and use of proactively delivered recommendations.

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Cited By

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  • (2024)Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life DataProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688068(654-659)Online publication date: 8-Oct-2024
  • (2024)Supporting Knowledge Workers through Personal Information Assistance with Context-aware Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688010(1296-1301)Online publication date: 8-Oct-2024
  • (2024)An approach for proactive mobile recommendations based on user-defined rulesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122714242:COnline publication date: 16-May-2024
  • Show More Cited By

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cover image ACM Conferences
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems
September 2016
490 pages
ISBN:9781450340359
DOI:10.1145/2959100
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 07 September 2016

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Author Tags

  1. content relevance
  2. modality
  3. proactive recommendation delivery
  4. recommender systems
  5. timing

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RecSys '16
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RecSys '16: Tenth ACM Conference on Recommender Systems
September 15 - 19, 2016
Massachusetts, Boston, USA

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RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2024)Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life DataProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688068(654-659)Online publication date: 8-Oct-2024
  • (2024)Supporting Knowledge Workers through Personal Information Assistance with Context-aware Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688010(1296-1301)Online publication date: 8-Oct-2024
  • (2024)An approach for proactive mobile recommendations based on user-defined rulesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122714242:COnline publication date: 16-May-2024
  • (2018)A probabilistic model for intrusive recommendation assessmentProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240403(441-445)Online publication date: 27-Sep-2018

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