Integrated personal recommender systems

Chung, Ronald; Sundaram, David; Srinivasan, Ananth

doi:10.1145/1282100.1282113

Cited by 29 publications

(16 citation statements)

References 15 publications

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“…We now introduce the different approaches to cross-domain RSs based on transfer-learning [49,50] This approach was first explored in a framework that selected items according to common attributes in the target and source domains [33]. The idea led to further research based on exploring user/item domain correlations via latent factors learning [5,10,16].…”

Section: Cross-domain Recommender Systems Based On Transfer Learningmentioning

confidence: 99%

Utilizing transfer learning for in-domain collaborative filtering

Grolman

Bar

Shapira

et al. 2016

Knowledge-Based Systems

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Section: Cross-domain Recommender Systems Based On Transfer Learningmentioning

confidence: 99%

Utilizing transfer learning for in-domain collaborative filtering

Grolman

Bar

Shapira

et al. 2016

Knowledge-Based Systems

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“…This information should be collected before, during, and after the travel [26]. Moreover, according to the integral theory of Wilber [27], all of the reality in the world neither is composed of whole/parts, not just whole nor parts (holistic perspectives).…”

Section: Atrs Problems and Issuesmentioning

confidence: 99%

Adaptive tourist recommendation system: conceptual frameworks and implementations

Etaati

Sundaram

2014

Vietnam J Comput Sci

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Recommendation systems and adaptive systems have been introduced in travel applications to support the travelers in their decision-making processes. These systems should respond to the unexpected changes during the travel. As a result, the first step is to sense the traveler's specifications, needs, and preferences before, during, and after the travel. Moreover, they should gather information about the accommodations, flights, cities, activities, and destinations through the different sources. In the next stage, these systems should provide personalized information. However, current tourist systems are not able to collect all travelers and travel products information from different resources. In addition, they failed to provide a sequence of recommendation on the different travel products (i.e., main destination, desired budget, length of travel, accommodation, transportation, activity, and restaurant) based on the traveler order preferences and travel stage. These systems do not support any customization. For instance, expert traveler or system admin could not change the recommendation algorithm settings anymore. To address these problems and issues, we propose and implement an adaptive tourist recommendation system that is supported by an adaptive tourist recommendation framework, process, architecture, and system.

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“…Multiple ways for integrating user models in adaptive learning systems, collected by multiple educational systems is provided in [14]. Cross-item mediation is one of popular ways applied to existing recommendation techniques, such as content-based filtering [17], collaborative filtering [11], and hybrid approaches [3]. This mediation technique assumes that the items are similar to the past items, including cross-items from the other remote systems users liked, should be recommended to them.…”

Section: Related Workmentioning

confidence: 99%

Recommending research colloquia

Sahebi

Wongchokprasitti

Brusilovsky

2010

Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems

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The study reported in this paper is an attempt to improve contentbased recommendation in CoMeT, a social system for sharing information about research colloquia in Carnegie Mellon and University of Pittsburgh campuses. To improve the quality of recommendation in CoMeT, we explored three additional sources for building user profiles: tags used by users to annotate CoMeT's talks, partial content of CiteULike papers bookmarked by users, and tags used to annotate CiteULike papers. We also compare different tag integration models to study the impact of information fusion on recommendations outcome. The results demonstrate that information encapsulated in CiteULike bookmarks generally helps to improve several aspects of recommendation. The addition of tags by fusing them into keyword profiles helps to improve precision and novelty of recommendation, but may harm systems ability to recommend generally interesting talks. The effects of tags and bookmarks appeared to be stackable.

show abstract

Integrated personal recommender systems

Cited by 29 publications

References 15 publications

Utilizing transfer learning for in-domain collaborative filtering

Utilizing transfer learning for in-domain collaborative filtering

Adaptive tourist recommendation system: conceptual frameworks and implementations

Recommending research colloquia

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