scholarly journals Feature-Weighted User Model for Recommender Systems

2007 ◽  
pp. 97-106 ◽  
Author(s):  
Panagiotis Symeonidis ◽  
Alexandros Nanopoulos ◽  
Yannis Manolopoulos
Keyword(s):  
Recommender Systems ◽  
User Model

scholarly journals User Modeling from Review Browsing History for Personal Values-Based Recommendation

2020 ◽  
Vol 24 (3) ◽  
pp. 326-334
Author(s):  
Yasufumi Takama ◽  
◽  
Suzuto Shimizu
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
User Modeling ◽  
Personal Values ◽  
User Model ◽  
User Feedback ◽  
Modeling Method ◽  
History Of

This paper proposes a personal values-based user modeling method from user’s browsing history of reviews. Personal values-based user modeling and its application to recommender systems have been studied. This approach models users’ personal values as the effect of item’s attributes on their decision making. While existing method obtains a user model from reviews posted by a user, this paper proposes to obtain it from reviews a user consulted for his/her decision making. Methods for determining reviews to present for obtaining user feedback, as well as for selecting items to recommend are proposed, of which effectiveness are shown with user experiments.

scholarly journals User models for multi-context-aware music recommendation

2020 ◽  
Author(s):  
Martin Pichl ◽  
Eva Zangerle
Keyword(s):  
Recommender Systems ◽  
User Model ◽  
Context Aware ◽  
Music Recommendation ◽  
Music Consumption ◽  
Current Context ◽  
Music Recommender Systems ◽  
Context Features ◽  
Content Information ◽  
Music Streaming

Abstract In the last decade, music consumption has changed dramatically as humans have increasingly started to use music streaming platforms. While such platforms provide access to millions of songs, the sheer volume of choices available renders it hard for users to find songs they like. Consequently, the task of finding music the user likes is often mitigated by music recommender systems, which aim to provide recommendations that match the user’s current context. Particularly in the field of music recommendation, adapting recommendations to the user’s current context is critical as, throughout the day, users listen to different music in numerous different contexts and situations. Therefore, we propose a multi-context-aware user model and track recommender system that jointly exploit information about the current situation and musical preferences of users. Our proposed system clusters users based on their situational context features and similarly, clusters music tracks based on their content features. By conducting a series of offline experiments, we show that by relying on Factorization Machines for the computation of recommendations, the proposed multi-context-aware user model successfully leverages interaction effects between user listening histories, situational, and track content information, substantially outperforming a set of baseline recommender systems.

GUMCARS: General User Model for Context-Aware Recommender Systems

2018 ◽  
Vol 37 (5) ◽  
pp. 1149-1183
Author(s):  
Sergio Inzunza ◽  
Reyes Juárez-Ramírez ◽  
Samantha Jiménez ◽  
Guillermo Licea
Keyword(s):  
Recommender Systems ◽  
User Model ◽  
Context Aware ◽  
General User

Semantic User Model Inferences for Travel Recommender Systems

2010 ◽  
pp. 23-37
Author(s):  
Yanwu Yang
Keyword(s):  
Recommender Systems ◽  
Urban Space ◽  
Web Application ◽  
Description Logic ◽  
User Model ◽  
Model Based ◽  
Travel Recommender Systems ◽  
Spatial Entities

This chapter proposes a semantic user model based on a description logic language to represent user’s knowledge and information, and a set of domain-dependent rules specific to the tourism domain in terms of spatial criteria (i.e., distance) and cognition to infer useful user features such as interests and preferences as important inputs for travel recommender systems (TRS). We also identify a spatial Web application scenario in the tourism domain, which is intended to provide personalized information about a variety of spatial entities in order to assist the user in traveling in an urban space.

scholarly journals Estimating a latent-class user model for travel recommender systems

2018 ◽  
Vol 19 (1-4) ◽  
pp. 61-82 ◽  
Author(s):  
Theo Arentze ◽  
Astrid Kemperman ◽  
Petr Aksenov
Keyword(s):  
Recommender Systems ◽  
Latent Class ◽  
User Model ◽  
Travel Recommender Systems

scholarly journals A New User Model based Interactive Product Retrieval Process for improved eBuying

2015 ◽  
Vol 19 ◽  
Author(s):  
Oshadi Alahakoon
Keyword(s):  
Information Retrieval ◽  
Recommender Systems ◽  
User Model ◽  
User Preferences ◽  
Retrieval Algorithm ◽  
Retrieval Process ◽  
Retrieval Systems ◽  
User Query ◽  
Information Retrieval Systems ◽  
Interactive Product

When searching for items online there are three common problems that e-buyers may encounter; null retrieval, retrieving unmanageable number of items, and retrieving unsatisfactory items. In the past information retrieval systems or recommender systems were used as solutions. With information retrieval systems, too rigorous filtering based on the user query to reduce unmanageable number of items result in either null retrieval or filtering out the items users prefer. Recommender systems on the other hand do not provide sufficient opportunity for users to communicate their needs. As a solution, this paper introduces a novel method combining a user model with an interactive product retrieval process. The new layered user model has the potential of being applied across multiple product and service domains and is able to adapt to changing user preferences. The new product retrieval algorithm is integrated with the user model and is able to successfully address null retrieval, retrieving unmanageable number of items, and retrieving unsatisfactory items. The process is demonstrated using a bench mark dataset and a case study. Finally the Product retrieval process is evaluated using a set of guidelines to illustrate its suitability to current eBuying environments.

Fuzzy-genetic approach to recommender systems based on a novel hybrid user model

2008 ◽  
Vol 35 (3) ◽  
pp. 1386-1399 ◽  
Author(s):  
Mohammad Yahya H. Al-Shamri ◽  
Kamal K. Bharadwaj
Keyword(s):  
Recommender Systems ◽  
User Model ◽  
Genetic Approach ◽  
Fuzzy Genetic
Sign in / Sign up

Export Citation Format

Share Document