Collaborative filtering recommendation algorithm based on user preference derived from item domain features

Zhang, Jing; Peng, Qinke; Sun, Shuang; Liu, Che

doi:10.1016/j.physa.2013.11.013

Cited by 76 publications

(38 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the said three approaches, users are collected and for each user, the neighborhood of users who posted her same articles and the neighborhood of users who share the same tags are included in this collection. [33] In Social tagging system, user-created tags are utilized to depict user preferences for personalized recommendation but it is difficult to identify users with similar interests due to the difference between users' descriptive habits and the diversity of language expression. So item domain features are utilized to construct user preference models and combined with CF for personalized recommendation.…”

Section: User-based Collaborative Filtering [5]mentioning

confidence: 99%

A Survey on Decision Support Systems in Social Media

Thangaraj¹,

Indra²

2015

IJCATR

View full text Add to dashboard Cite

Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about "feeding you the information that you want, when you want it" i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms semantic technologies and social computing environment. Recommender system is a subclass of decision support system. Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified recommendations along with semantic web and also discussed about major thrust areas of research in each category.

show abstract

Section: User-based Collaborative Filtering [5]mentioning

confidence: 99%

A Survey on Decision Support Systems in Social Media

Thangaraj¹,

Indra²

2015

IJCATR

View full text Add to dashboard Cite

show abstract

“…The model not only considered the local context information of user ratings, but also the global preference of user behavior. In order to find a better way to depict user preferences to make it more suitable for personalized recommendation, Zhang et al 8 introduced a framework that utilized item domain features to construct user preference models and combined these models with collaborative filtering (CF). The framework not only integrated domain characteristics into a personalized recommendation, but also aided to detecting the implicit relationships among users, which were missed by the conventional CF method.…”

Section: Related Workmentioning

confidence: 99%

Efficient Collaborative Filtering Using Particle Swarm Optimization and K-Harmonic Means Algorithm

Qiang

2015

j comput theor nanosci

View full text Add to dashboard Cite

Recommender systems have emerged in response to the problem of information overload and can help users to find their interest content. In order to alleviate the sparsity problem of recommender systems and increase the accuracy and diversity of recommendation results, we propose an effective collaborative filtering recommendation method. We adopt PSO-KHM algorithm which composes of Particle Swarm Optimization (PSO) and K-Harmonic means (KHM) to cluster users and then use improved collaborative filtering algorithm to compute the similarity between these clustered users. Further more, we take into account many influential factors in the process of similarity computation. The simulation results on two real-world datasets show that our algorithm achieves superior performance to existing methods.

show abstract

“…In another example, Tsai & Chung (2012) leveraged the use of the RFID technology to track the behaviour of a theme park's visitors for identifying route patterns that were matched to the input the visitors provided, so that a route recommendation system was built that suggested the newly arrived visitors the most suitable visit programme based on the experience of previous visitors with similar profiles 7 . Other examples involve, e.g., an analysis and exchange of information about the road situation in self-drive tourists' vicinity in offering them real-time personalised route recommendation 9 or the use of item domain features for generating user preference models to be involved in domain recommendation 10 . Besides, recent advances in technology allow the creators of recommender systems for tourists to employ high-standard graphical solutions and thereby further improve tourists' experience.…”

Section: Recommender Systems For Touristsmentioning

confidence: 99%

Toward Personalised and Dynamic Cultural Routing: A Three-Level Approach

Aksenov

Kemperman

Arentze

2014

Procedia Environmental Sciences

View full text Add to dashboard Cite

This paper introduces the concept of "smart routing" as a recommender system for tourists that takes into account the dynamics of their personal user profiles. The concept relies on three levels of support: 1) programming the tour, i.e. selecting a set of relevant points of interests (POIs) to be included into the tour, 2) scheduling the tour, i.e. arranging the selected POIs into a sequence based on the cultural, recreational and situational value of each, and 3) determining the tour's travel route, i.e. generating a set of trips between the POIs that the tourist needs to perform in order to complete the tour. The "smart routing" approach intends to enhance the experience of tourists in a number of ways. The first advantage is the system's ability to reflect on the tourists' dynamic preferences, for which an understanding of the influence of a tourist's affective state and dynamic needs on the preferred activities is required. Next, it arranges the POIs together in a way that creates a storyline that the tourist will be interested to follow, which adds to the tour's cultural value. Finally, the POIs are connected by a chain of multimodal trips that the tourist will have to make, also in accordance with the tourist's preferences and dynamic needs. As a result, each tour can be personalised in a "smart" way, from the perspective of both the cultural and the overall experience of taking it. We present the building blocks of the "smart routing" concept in detail and describe the data categories involved. We also report on the current status of our activities with respect to the inclusion of a tourist's affective state and dynamic needs into the preference measurement phase, as well as discuss relevant practical concerns in this regard.

show abstract

Collaborative filtering recommendation algorithm based on user preference derived from item domain features

Cited by 76 publications

References 26 publications

A Survey on Decision Support Systems in Social Media

A Survey on Decision Support Systems in Social Media

Efficient Collaborative Filtering Using Particle Swarm Optimization and K-Harmonic Means Algorithm

Toward Personalised and Dynamic Cultural Routing: A Three-Level Approach

Contact Info

Product

Resources

About