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Cold-Start Recommendation for On-Demand Cinemas
2020The on-demand cinemas, which has emerged in recent years, provide offline entertainment venues for individuals and small groups. Because of the limitation of network speed and storage space, it is necessary to recommend movies to cinemas, that is, to suggest cinemas to download the recommended movies in advance.
Beibei Li 0001 +5 more
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A hybrid approach for cold start recommendations
2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), 2017The work presented in this paper is part of the system developed by our team for RecSys challenge 2017. XING is a social networking website that allows the recruiters to post their job openings. It also allows the interested candidates to look up these job openings and apply for the job if suits their requirements.
Madhulika Agrawal +2 more
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On cold start for associative tag recommendation
Journal of the Association for Information Science and Technology, 2015Tag recommendation strategies that exploit term co‐occurrence patterns with tags previously assigned to the target object have consistently produced state‐of‐the‐art results. However, such techniques work only for objects with previously assigned tags.
Eder Ferreira Martins +3 more
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Computation of Cold-Start Miss Ratios
IEEE Transactions on Computers, 1978Cold-start miss ratios are miss ratios that are measured with an initially empty first-level store. The values obtained depend on C, the first-level storage capacity, and on T, the number of references. These miss ratios, measured for various values of T, are useful in evaluating the effect of task switching on cache miss ratios when the cache capacity
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Warming Up to Cold Start Personalization
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018Smart agents face abandonment if they are unable to provide value to the users from the very first interaction. Existing smart agents take time to learn about new users before they can offer them personalized services. We present a method for learning personalization information about users quickly and without placing unnecessary hardship on them.
Nikola Banovic 0001, John Krumm
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Searching to Modulate for Cold-Start Recommendation
IEEE Transactions on Pattern Analysis and Machine IntelligenceMaking personalized recommendation for cold-start users, who only have a few interaction histories, is a challenging problem in recommendation systems. Recent works leverage hypernetworks to directly map interaction histories to user-specific parameters, which are then used to modulate predictor by certain modulation structure.
Shiguang Wu 0002 +2 more
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Methods and metrics for cold-start recommendations
Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, 2002We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naive Bayes classifier on the cold-start problem, where we wish to recommend items that no one in the community has yet rated.
Andrew I. Schein +3 more
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Scientific Lubrication, 1949
The modern motorist rightly expects to use his car under all weather conditions. In fact, its utility is greatest in bad weather. It is, therefore, essential that he—and his wife—should be able to start the engine promptly and drive away in cold weather with the minimum of trouble.
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The modern motorist rightly expects to use his car under all weather conditions. In fact, its utility is greatest in bad weather. It is, therefore, essential that he—and his wife—should be able to start the engine promptly and drive away in cold weather with the minimum of trouble.
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Cold-start solutions for recommendation systems
2019Recommendation systems are essential tools to overcome the choice overload problem by suggesting items of interest to users. However, they suffer from a major challenge which is the so-called cold-start problem. The cold-start problem typically happens when the system does not have any form of data on new users and on new items.
Moghaddam, Farshad Bakhshandegan +1 more
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Cold Start Problem: A Lightweight Approach
2013The chapter presents the SWAPTeam participation at the ECML/PKDD 2011 - Discovery Challenge for the task on the cold start problem focused on making recommendations for new video lectures. The developed solution uses a content-based approach because it is less sensitive to the cold start problem that is commonly associated with pure collaborative ...
Iaquinta L. +2 more
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