Personalized recommendation via user preference matching

Graph-based recommendation approaches use a graph model to represent the relationships between users and items, and exploit the graph structure to make recommendations. Recent graph-based recommendation approaches focused on capturing users’ pairwise preferences and utilized a graph model to exploit the relationships between different entities in the graph. In this paper, we focus on the impact of pairwise preferences on the diversity of recommendations. We propose a novel graph-based ranking oriented recommendation algorithm that exploits both explicit and implicit feedback of users. The algorithm utilizes a user-preference-item tripartite graph model and modified resource allocation process to match the target user with users who share similar preferences, and make personalized recommendations. The principle of the additional preference layer is to capture users’ pairwise preferences, provide detailed information of users for further recommendations. Empirical analysis of four benchmark datasets demonstrated that our proposed algorithm performs better in most situations than other graph-based and ranking-oriented benchmark algorithms.     

A probabilistic music recommender considering user opinions and audio features

A recommender system has an obvious appeal in an environment where the amount of on-line information vastly outstrips any individual’s capability to survey. Music recommendation is considered a popular application area. In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focus on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to utilize information extracted directly from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. In addition, this model has been extended for improved recommendation performance by utilizing audio features that help alleviate three well-known problems associated with data sparseness in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experimental results based on two real-world data sets lead us to believe that content information is crucial in achieving better personalized recommendation beyond user ratings. We further show how primitive audio features can be combined into aggregate features for the proposed CRMS and analyze their influences on recommendation performance. Although this model was developed originally for music collaborative recommendation based on audio features, our experiment with the movie data set demonstrates that it can be applied to other domains.     

Supporting ease-of-use and user control: desired features and structure of Web-based online IR systems

The emergence of Web-based IR systems calls for the need to support ease-of-use as well as user control. This study attempts to investigate users’ perceptions of ease-of-use versus user control, and desired functionalities as well as desired interface structure of online IR systems in supporting both ease-of-use and user control. Forty subjects who had an opportunity to learn and use five online databases participated in the study. Multiple methods were employed to collect data. The qualitative and quantitative analysis of the data show that users consider both ease-of-use and user control are essential for effective retrieval. The results are discussed within the context of a model of optimal support for ease-of-use and user control, particularly, emphasizing on the balance between system role and user involvement in achieving various IR sub-tasks.     

Search history support for finding and using information: User interface design recommendations from a user study

Recording search histories, presenting them to the searcher, and building additional interface tools on them offer many opportunities for supporting user tasks in information seeking and use. This study investigated the use of search history information in legal information seeking. Qualitative methods were used to explore how attorneys and law librarians used their memory and external memory aids while searching for information and in transferring to information use. Based on the findings, interface design recommendations were made for information systems.     

TDTMF: A recommendation model based on user temporal interest drift and latent review topic evolution with regularization factor

This paper constructs a novel enhanced latent semantic model based on users’ comments, and employs regularization factors to capture the temporal evolution characteristics of users’ potential topics for each commodity, so as to improve the accuracy of recommendation. The adaptive temporal weighting of multiple preference features is also improved to calculate the preferences of different users at different time periods using human forgetting features, item interest overlap, and similarity at the semantic level of the review text to improve the accuracy of sparse evaluation data. The paper conducts comparison experiments with six temporal matrix-based decomposition baseline methods in nine datasets, and the results show that the accuracy is 31.64% better than TimeSVD++, 21.08% better than BTMF, 15.51% better than TMRevCo, 13.99% better than BPTF, 9.24% better than TCMF, and 3.19% better than MUTPD ,which indicates that the model is more effective in capturing users’ temporal interest drift and better reflects the evolutionary relationship between users’ latent topics and item ratings.     

Domain analysis and information retrieval through the construction of heliocentric maps based on ISI-JCR category cocitation

We propose the use of ISI-JCR categories as units of cocitation and measurement for the construction of heliocentric maps. The use of a spatial metaphor allows us to illustrate, analyze and compare domains in terms of the categories and their interconnections or links. We can also move around within the structure of these domains for further analysis, and access the documents associated to the categories and to the links that cocite or relate them.     

Building user profiles based on sequences for content and collaborative filtering

Modeling user profiles is a necessary step for most information filtering systems – such as recommender systems – to provide personalized recommendations. However, most of them work with users or items as vectors, by applying different types of mathematical operations between them and neglecting sequential or content-based information. Hence, in this paper we study how to propose an adaptive mechanism to obtain user sequences using different sources of information, allowing the generation of hybrid recommendations as a seamless, transparent technique from the system viewpoint. As a proof of concept, we develop the Longest Common Subsequence (LCS) algorithm as a similarity metric to compare the user sequences, where, in the process of adapting this algorithm to recommendation, we include different parameters to control the efficiency by reducing the information used in the algorithm (preference filter), to decide when a neighbor is considered useful enough to be included in the process (confidence filter), to identify whether two interactions are equivalent (δ-matching threshold), and to normalize the length of the LCS in a bounded interval (normalization functions). These parameters can be extended to work with any type of sequential algorithm.We evaluate our approach with several state-of-the-art recommendation algorithms using different evaluation metrics measuring the accuracy, diversity, and novelty of the recommendations, and analyze the impact of the proposed parameters. We have found that our approach offers a competitive performance, outperforming content, collaborative, and hybrid baselines, and producing positive results when either content- or rating-based information is exploited.     

A user term visualization analysis based on a social question and answer log

The authors of this paper investigate terms of consumers’ diabetes based on a log from the Yahoo!Answers social question and answers (Q&A) forum, ascertain characteristics and relationships among terms related to diabetes from the consumers’ perspective, and reveal users’ diabetes information seeking patterns. In this study, the log analysis method, data coding method, and visualization multiple-dimensional scaling analysis method were used for analysis. The visual analyses were conducted at two levels: terms analysis within a category and category analysis among the categories in the schema. The findings show that the average number of words per question was 128.63, the average number of sentences per question was 8.23, the average number of words per response was 254.83, and the average number of sentences per response was 16.01. There were 12 categories (Cause & Pathophysiology, Sign & Symptom, Diagnosis & Test, Organ & Body Part, Complication & Related Disease, Medication, Treatment, Education & Info Resource, Affect, Social & Culture, Lifestyle, and Nutrient) in the diabetes related schema which emerged from the data coding analysis. The analyses at the two levels show that terms and categories were clustered and patterns were revealed. Future research directions are also included.     

基于主题图与MARC的书目可视化检索系统设计与实现

融合MARC、主题图与可视化技术,从设计步骤、实现方法两方面阐述基于主题图与MARC的书目可视化检索系统构建原理,并实现系统原型。系统以可视化的方式展示书籍间联系,有利于用户通过联系查找更多书籍。     

Willingness to pay and experienced utility as measures of affective value of information objects: Users’ accounts

The article discusses two instant measures of affective value of information objects: Willingness-to-Pay (WTP) and Experienced Utility (EU). The potential utility of these measures for information retrieval research is positioned within a broader discussion of distinctions between instant utility and remembered utility and their relationship to immediate as opposed to retrospective subjective evaluations. The paper describes an approach for obtaining instant WTP and EU ratings in document evaluation in contexts of information search and thereafter reports results from a usability study of its implementation. Participants encountered no difficulties in use of the design format and in rating documents using WTP and EU measures. Although two measures were found statistically significantly correlated with each other, participants in the usability study offered different reasons for the ratings of WTP and EU made for the same document.     

科学学理论体系建构的思考——基于科学计量学的中外科学学进展研究报告

近10年来,国内外科学学的发展正在形成科学学理论体系的新框架。本文从科学学的研究传统与范式入手,运用科学计量学一系列方法和手段,对中外科学学研究的主要领域、相关学科以及热点问题进行了深入的考察和比较,结果表明以科学、技术、创新以及相关的科技政策与管理为内容的科学学应用研究是当前的主流领域;网络计量学、科学知识图谱和信息可视化技术发展迅猛,正成为科学学方法研究的前沿领域;有关科学、技术与创新相互作用规律、自然科学某些学科的演化规律以及科学技术合作理论等成为科学学理论研究的热门领域。在此基础上,对新世纪科学学理论体系按“研究维-学科维-对象维”的三维结构建构进行了初步探讨。