移动设备的个性化推荐在上下文感知应用

个性化推荐对上下文感知系统具有广泛而重要应用,现在大多数个性化推荐系统很少考虑用户的认知风格.文中比较了场独立用户和场依赖用户在上下文感知环境中所存在的差异,提出一种基于用户认知风格的个性化推荐系统(CSBRS).通过CSBRS的实现例子,表明基于用户认知风格的个性化推荐有助于提高个性化推荐的质量.     

Ranking canonical views for tourist attractions

Online photo collections have become truly gigantic. Photo sharing sites such as Flickr (http://www.flickr.com/) host billions of photographs, a large portion of which are contributed by tourists. In this paper, we leverage online photo collections to automatically rank canonical views for tourist attractions. Ideal canonical views for a tourist attraction should both be representative of the site and exhibit a diverse set of views (Kennedy and Naaman, International Conference on World Wide Web 297–306, 2008). In order to meet both goals, we rank canonical views in two stages. During the first stage, we use visual features to encode the content of photographs and infer the popularity of each photograph. During the second stage, we rank photographs using a suppression scheme to keep popular views top-ranked while demoting duplicate views. After a ranking is generated, canonical views at various granularities can be retrieved in real-time, which advances over previous work and is a promising feature for real applications. In order to scale canonical view ranking to gigantic online photo collections, we propose to leverage geo-tags (latitudes/longitudes of the location of the scene in the photographs) to speed up the basic algorithm. We preprocess the photo collection to extract subsets of photographs that are geographically clustered (or geo-clusters), and constrain the expensive visual processing within each geo-cluster. We test the algorithm on two large Flickr data sets of Rome and the Yosemite national park, and show promising results on canonical view ranking. For quantitative analysis, we adopt two medium data sets and conduct a subjective comparison with previous work. It shows that while both algorithms are able to produce canonical views of high quality, our algorithm has the advantage of responding in real-time to canonical view retrieval at various granularities.     

Integrating tourist packages and tourist attractions for personalized trip planning based on travel constraints

In recent years, with the upgrading of mobile positioning and the popularity of smart devices, location related research gets a lot of attentions. One of popular issues is the trip planning problem. Although many related scientific or technical literature have been proposed, most of them focused only on tourist attraction recommendation or arrangement meeting some user demands. In fact, to grasp the huge tourism opportunities, more and more tour operators design tourist packages and provide to users. Generally, tourist packages have many advantages such as cheaper ticket price and higher transportation convenience. However, researches on trip planning combining tourist packages have not been mentioned in the past studies. In this research, we present a new approach named Package-Attraction-based Trip Planner (PAT-Planner) to simultaneously combine tourist packages and tourist attractions for personalized trip planning satisfying users’ travel constraints. In PAT-Planner, we first based on user preferences and temporal characteristics to design a Score Inference Model for respectively measuring the score of a tourist package or tourist attraction. Then, we develop the Hybrid Trip-Mine algorithm meeting user travel constraints for personalized trip planning. Besides, we further propose two improvement strategies, namely Score Estimation and Score Bound Tightening, based on Hybrid Trip-Mine to speed up the trip planning efficiency. As far as we know, our study is the first attempt to simultaneously combine tourist packages and tourist attractions on trip planning problem. Through a series of experimental evaluations and case studies using the collected Gowalla datasets, PAT-Planner demonstrates excellent planning effects.     

Turist@: Agent-based personalised recommendation of tourist activities

Recommender systems in e-Tourism normally focus on helping tourists to select appropriate destinations. A related problem that has been less explored in the literature is how to provide personalised recommendations of cultural and leisure activities when the tourist has already arrived at the destination. This paper presents a novel recommendation system, Turist@, which addresses this issue. Its agent-based modular design permits to model different kinds of activities in a flexible way, and allows the implementation of a location-aware front-end in the mobile device of the user. Special care has been put in the recommendation engine, implemented via a specialised Recommender Agent. It incorporates a mixture of content-based and collaborative recommendation strategies, thus avoiding the drawbacks of each individual method, and is able to perform recommendations in heterogeneous scenarios. Recommendations take into account user profiles which are implicitly updated after the analysis of user actions (e.g., queries, evaluations). The system has been successfully deployed and tested in the World Heritage-listed city of Tarragona.     

第三方电子商务的个性化信息推荐系统

在分析已有成果的基础上,设计了一个面向第三方电子商务的个性化信息推荐系统,并详细阐述了该系统的体系结构、功能划分以及关键技术.该系统通过追踪用户的阅读行为、分析用户的喜好,进而学习用户的兴趣和行为,实现了主动向用户推荐个性化信息,个性化评比、个性化主题分类及版面配置的功能.实验结果表明,该个性化信息推荐系统具有较好的性能.     

A web-based pervasive recommendation system for mobile tourist guides

Mobile tourist guides have attracted considerable research interest during the past decade, resulting in numerous standalone and web-based mobile applications. Particular emphasis has been given to personalization of services, typically based on travel recommender systems used to assist tourists in choosing places to visit; these systems address an important aspect of personalization and hence reduce the information burden for the user. However, existing systems fail to exploit information, behaviours, evaluations or ratings of other tourists with similar interests, which would potentially provide ground for the cooperative production of improved tourist content and travel recommendations. In this paper, we extend this notion of travel recommender systems utilizing collaborative filtering techniques while also taking into account contextual information (such as the current user’s location, time, weather conditions and places already visited by the user) for deriving improved recommendations in pervasive environments. We also propose the use of wireless sensor network (WSN) installations around tourist sites for enabling precise localization and also providing mobile users convenient and inexpensive means for uploading tourist information and ratings about points of interest (POI) via their mobile devices. We also introduce the concept of ‘context-aware rating’, whereby user ratings uploaded through WSN infrastructures are weighted higher to differentiate among users that rate POIs using the mobile tourist guide application while onsite and others using the Internet away from the POI.     

Personalized recommendation of popular blog articles for mobile applications

Weblogs have emerged as a new communication and publication medium on the Internet for diffusing the latest useful information. Providing value-added mobile services, such as blog articles, is increasingly important to attract mobile users to mobile commerce, in order to benefit from the proliferation and convenience of using mobile devices to receive information any time and anywhere. However, there are a tremendous number of blog articles, and mobile users generally have difficulty in browsing weblogs owing to the limitations of mobile devices. Accordingly, providing mobile users with blog articles that suit their particular interests is an important issue. Very little research, however, has focused on this issue.In this work, we propose a novel Customized Content Service on a mobile device (m-CCS) to filter and push blog articles to mobile users. The m-CCS includes a novel forecasting approach to predict the latest popular blog topics based on the trend of time-sensitive popularity of weblogs. Mobile users may, however, have different interests regarding the latest popular blog topics. Thus, the m-CCS further analyzes the mobile users’ browsing logs to determine their interests, which are then combined with the latest popular blog topics to derive their preferred blog topics and articles. A novel hybrid approach is proposed to recommend blog articles by integrating personalized popularity of topic clusters, item-based collaborative filtering (CF) and attention degree (click times) of blog articles. The experiment result demonstrates that the m-CCS system can effectively recommend mobile users’ desired blog articles with respect to both popularity and personal interests.     

移动电话内容服务系统的个性化推荐

移动电话内容服务系统允许移动用户通过移动互联技术浏览、购买和下载系统内容,是当前移动增值领域研究的热点。具有较强的时空灵活性,但在信息浏览、查找方面存在明显的局限性。提出了一个基于移动电话内容服务系统的个性化推荐系统．介绍了从寻找目标用户到实现推荐的全过程。实验结果表明。所介绍的个性化推荐系统可以有助于解决内容服务系统用户访问受限、资源迷茫的问题。     

PENETRATE: Personalized news recommendation using ensemble hierarchical clustering

Recommending online news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Many online readers have their own reading preference on news articles; however, a group of users might be interested in similar fascinating topics. It would be helpful to take into consideration the individual and group reading behavior simultaneously when recommending news items to online users. In this paper, we propose PENETRATE, a novel PErsonalized NEws recommendaTion framework using ensemble hieRArchical clusTEring to provide attractive recommendation results. Specifically, given a set of online readers, our approach initially separates readers into different groups based on their reading histories, where each user might be designated to several groups. Once a collection of newly-published news items is provided, we can easily construct a news hierarchy for each user group. When recommending news articles to a given user, the hierarchies of multiple user groups that the user belongs to are merged into an optimal one. Finally a list of news articles are selected from this optimal hierarchy based on the user’s personalized information, as the recommendation result. Extensive empirical experiments on a set of news articles collected from various popular news websites demonstrate the efficacy of our proposed approach.     

A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet

Selecting tourist attractions to visit at a destination is a main stage in planning a trip. Although various online travel recommendation systems have been developed to support users in the task of travel planning during the last decade, few systems focus on recommending specific tourist attractions. In this paper, an intelligent system to provide personalized recommendations of tourist attractions in an unfamiliar city is presented. Through a tourism ontology, the system allows integration of heterogeneous online travel information. Based on Bayesian network technique and the analytic hierarchy process (AHP) method, the system recommends tourist attractions to a user by taking into account the travel behavior both of the user and of other users. Spatial web services technology is embedded in the system to provide GIS functions. In addition, the system provides an interactive geographic interface for displaying the recommendation results as well as obtaining users’ feedback. The experiments show that the system can provide personalized recommendations on tourist attractions that satisfy the user.     

Personalized multi-modality image management and search for mobile devices

Mobile devices are quickly becoming a primary medium for personal information gathering, management, and sharing. Managing personal image data on mobile platforms is a challenging problem due to large data set size, content and context diversity, heterogeneous individual usage patterns, and resource constraints. This article presents a user-centric system, called iScope, for personal image management and sharing on mobile devices. iScope uses multi-modality clustering of both content and context information for efficient image management and search, and online learning techniques for predicting images of interest. It also supports distributed image search among networked devices while maintaining the same intuitive interface, enabling efficient information sharing among people. We have implemented iScope and conducted infield experiments using networked Nokia N810 portable Internet tablets. Energy efficiency was a primary design focus during the design and implementation of the iScope search algorithms. Experimental results demonstrate that iScope improves search time and search energy by 4.1× and 3.8× on average, relative to browsing.     

A comparative study of four input devices for desktop virtual walkthroughs

This paper presents the results of an experiment measuring the effect of four different input devices on overall task performance for desktop virtual walkthroughs. The input devices tested are: a keyboard, a mouse, a joystick and a gamepad. The results indicate that the participants completed the tasks in significantly less time and distance travelled with the mouse than with the three other input devices. The use of the mouse also significantly reduced the number of collisions, while the use of the gamepad resulted in significantly more collisions.     

Mobile Medicine: semantic computing management for health care applications on desktop and mobile devices

In many health care situations, powerful mobile tools may help to make decisions and provide support for continuous education and training. They can be useful in emergency conditions and for the supervised application of protocols and procedures. To this end, content models and formats with semantic and intelligence have more flexibility to provide medical personnel (both in off-line and on-line conditions) with more powerful tools than those currently on the market. In this paper, we are presenting Mobile Medicine solution, which exploits a collection of semantic computing technologies together with intelligent content model and tools to provide innovative services for medical personnel. Most of the activities of semantic computing are performed on the back office on a cloud computing architecture for: clustering, recommendations, intelligent content production and adaptation. The mobile devices have been endowed with a content organizer to collect local data, provide local suggestions, while supporting taxonomical searches and local queries on PDA and iPhone. The proposed solution is under usage at the main hospital in Florence. The smart content has been produced by medical personnel, with the adoption of the new ADF-Design authoring tool, which produces content in MPEG-21 format. The mobile content distribution service is integrated with a collaborative networking portal, for discussion on procedures and content, thus suggestions are provided on both PC and Mobiles (PDA and iPhone).     

Input devices for web browsing: age and hand effects

The work reported in this paper examined performance on a mixed pointing and data entry task using direct and indirect positioning devices for younger, middle-aged, and older adults (n=72) who were experienced mouse users. Participants used both preferred and non-preferred hands to perform an item selection and text entry task simulating a typical web page interaction. Older adults performed more slowly than middle-aged adults who in turn performed more slowly than young adults. Performance efficiency was superior with the mouse for older adults only on the first two trial blocks. Thereafter mouse and light pen yielded equivalent performance. For other age groups, mouse and light pen were equivalent at all points of practice. Contrary to prior research revealing superior performance with a light pen for pure pointing tasks, these results suggest that older adults may initially perform worse with a light pen than a mouse for mixed tasks.     

Personalized rough-set-based recommendation by integrating multiple contents and collaborative information

In recent years, explosively-growing information makes the users confused in making decisions among various kinds of products such as music, movies, books, etc. As a result, it is a challenging issue to help the user identify what she/he prefers. To this end, so called recommender systems are proposed to discover the implicit interests in user’s mind based on the usage logs. However, the existing recommender systems suffer from the problems of cold-start, first-rater, sparsity and scalability. To alleviate such problems, we propose a novel recommender, namely FRSA (Fusion of Rough-Set and Average-category-rating) that integrates multiple contents and collaborative information to predict user’s preferences based on the fusion of Rough-Set and Average-category-rating. Through the integrated mining of multiple contents and collaborative information, our proposed recommendation method can successfully reduce the gap between the user’s preferences and the automated recommendations. The empirical evaluations reveal that the proposed method, FRSA, can associate the recommended items with user’s interests more effectively than other existing well-known ones in terms of accuracy.     

A personalized route recommendation service for theme parks using RFID information and tourist behavior

Like any other industry, theme parks are now facing severe challenges from other entertainment competitors. To survive in a rapidly changing environment, creating high quality products/services in terms of consumer preference has become a critical issue for theme park managers. To fulfill these needs, this paper develops a route recommendation system that supplies theme park tourists with the facilities they should visit and in what order. In the proposed system, tourist behaviors (i.e. visiting sequences and corresponding timestamps) are persistently collected through a Radio-Frequency Identification (RFID) system and stored in a route database. The database is then segmented into sub-groups based on the similarity among tourists’ visiting sequences and time lengths. Whenever a visitor requests a route recommendation service, the system identifies the sub-group most similar to that visitor's personal preferences and intended visitation time. Based on the retrieved visiting behavior data and current facility queuing situation identified by the RFID system, the proposed system generates a proper route suggestion for the visitor. A simulation case is implemented to show the feasibility of the proposed system. Based on the experimental results, it is clear that the recommended route satisfies visitor requirements using previous tourists’ favorite experiences.     

Independent travel recommendation algorithm based on analytical hierarchy process and simulated annealing for professional tourist

Independent travelers, especially professional independent travelers, tend to plan their trip schedules according to their interests, preferred hotels, landmarks they wish to visit, budgets, time availability and various other factors. Hence, travel schedule planning is valuable for satisfying the unique needs of each traveler. In this paper, we propose an algorithm for independent travel recommendation, consisting of three steps. Firstly, landmarks in the destination are selected under the specific constraints, which is modeled as a 0-1 knapsack problem. Then, the landmarks will be evaluated comprehensively using AHP (Analytic Hierarchy Process) model, and the greedy simulated annealing algorithm is adopted to select the best landmarks with high evaluation scores. Next, with AHP-decision model, a most reasonable free line to the tourist destination is selected from multiple candidates. Lastly, the path planning among the landmarks is abstracted as a TSP (Travelling Sales Problem) problem, and the simulated annealing algorithm based on roulette wheel selection is adopted to solve it. Through simulation experiments, by comparing with package tour from the aspects of landmark selection, valid sightseeing time ratio, valid sightseeing consumption ratio and the tourist satisfaction, the proposed algorithm is evaluated and analyzed. Simulation results illustrate the feasibility and rationality of our approach, which can be used as an effective reference deciding individualized travel schedules and trip planning.     

Personalized web service recommendation through mishmash technique and deep learning model

Multimedia Tools and Applications - Discovering the relevant web services for specific applications in the dynamically changing business world becomes very critical. Researchers have used many...     

Personalized clothing recommendation combining user social circle and fashion style consistency

With the rapid expansion of social networks and fashion websites, clothing recommendation has attracted more attention of researchers, since various web data bring opportunities for recommender systems to solve the problems of cold start and sparsity. For clothing recommender system, user social circle and fashion style consistency of clothing items are two important factors, which have critical impacts on user decision making. In this paper, two practical problems are considered: how to visually analyze fashion style consistency between clothing items and how to implement personalized clothing recommendation by combining user social circle and fashion style consistency. To address the first problem, a Siamese Convolutional Neural Network (SCNN) with a novel sampling strategy is employed to measure the fashion style consistency of clothing items. It can learn a feature transformation from clothing images to a latent feature space, where the representations of clothing items with similar styles locate closer than those with different styles. For the second problem, three social factors (i.e., personal interest, interpersonal interest similarity and interpersonal influence) and fashion style consistency are fused into a unified personalized recommendation model based on probabilistic matrix factorization (PMF). To comprehensively evaluate our model, extensive experiments have been conducted on two real world datasets collected from a popular social fashion website, which demonstrate the effectiveness of the proposed method for personalized clothing recommendation.     

"互联网+"时代个性化试题推荐方法

"互联网+"环境下,网上自主学习已成常态.文章提出一种个性化试题推荐方法,先通过认知诊断模型诊断出学生的认知状态,再采用协同过滤方法推荐试题,能给学生推荐准确且可解释性的试题.