Does Daily Travel Pattern Disclose People’s Preference?

Existing studies normally focus on extracting temporal or periodical patterns of people’s daily travel for location based services. However, people’s characteristics and preference are actually paid much more attention by business. Therefore, how to capture characteristics from their daily travel patterns, is an interesting question. In order to address the research question, we first develop two basic measures in terms of repetitiveness of travel and then two advanced measures, to capture people’s activity of daily travel, and the colorfulness of lifestyle, respectively. Incorporating historical trajectories, with real-time positions from a location-based social network (LBSN), i.e. Foursquare, we conduct statistical analysis for people’s travel patterns in US cities. Finally, we illustrate people’s profiles of travel patterns and lifestyles. Results show that people’s preference can be inferred from the developed activity and colorfulness measures. Those findings demonstrate that proposed measures are supposed to be effectively adopted for researchers on travel pattern analysis and preference analysis, and further give suggestions to individuals for location-based decision making

PReFacTO: Preference Relations Based Factor Model with Topic Awareness and Offset

Recommendation systems create personalized list of items that might interest the user by analyzing the user’s history of past purchases and/or consumption. For rating based systems, most of the traditional methods for recommendation focus on the absolute ratings provided by the users to the items. In this paper, we extend the traditional Matrix Factorization approach for recommendation and propose pairwise relation based factor modeling. While modeling the items in the system, the use of pairwise preferences allow information flow between the items through the preference relations as an additional information. Item feedbacks are available in the form of reviews apart from the rating information. The reviews have textual information that can be really helpful to represent the item’s latent feature vector appropriately. We perform topic modeling of the item reviews and use the topic vectors to guide the joint factor modeling of the users and items and learn their final representations. The proposed method shows promising results in comparison to the state-of-the-art methods in our experiments

PReFacTO: Preference Relations Based Factor Model with Topic Awareness and Offset

Recommendation systems create personalized list of items that might interest the user by analyzing the user’s history of past purchases and/or consumption. Generally only a small subset of the items are assessed by each user, and from the large subset of unseen items, the systems need to produce an accurate list of recommendations. For rating based systems, most of the traditional methods for recommendation focus on the absolute ratings provided by the users to the items. In this work, we extend the traditional Matrix Factorization approach for recommendation and propose pairwise relation based factor modeling. We propose the method based on the pairwise preferences between the items to capture the relative tendency of user selecting one item over the other. While modeling the items in the system, the use of pairwise preferences allow information flow between the items through the preference relations as an additional information. Item feedbacks are available in the form of reviews apart from the rating information. The reviews have textual information that can be really helpful to represent the item’s latent feature vector appropriately. We perform topic modeling of the item reviews and use the topic vectors to guide the joint factor modeling of the users and items and learn their final representations. The proposed methods shows promising results in comparison to the state-of-the-art methods in our experiments. v

Preference Relation-based Markov Random Fields for Recommender Systems

Abstract A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings first, and then inferring the item rankings, based on the assumption of availability of explicit feedbacks such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and significantly improved Top-N recommendation performance has been achieved

Relative preference-based recommender systems

This study investigates the problem of making recommendations to users, such as recommending a movie. Several novel models are proposed to make accurate recommendations by analyzing both the explicit and implicit data. Experiment results have confirmed improvements over state-of-the-art models

Preference relation-based Markov random fields for recommender systems

© 2015 S. Liu, G. Li, T. Tran & Y. Jiang. A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings first, and then inferring the item rankings, based on the assumption of availability of explicit feedbacks such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and significantly improved Top-N recommendation performance has been achieved

Erratum to: Preference Relation-based Markov Random Fields for Recommender Systems (Mach Learn, 10.1007/s10994-016-5603-7)

© 2017, The Author(s). In the Acknowledgements section, the Grant Number should be 61673201 instead of 61273301