User Preference Profile Research Articles

Item feature refinement using matrix factorization and boosted learning based user profile generation for content-based recommender systems

A content-based recommender system uses essential item features that play a crucial role in building quality user preference profiles. However, in most real-world datasets, the item features are highly inconsistent and sparse, making it challenging to develop efficient user profiles. Additionally, the user preference profiles created by individual learners fail to learn from the misclassification of user ratings and preferences. Thus, to resolve these problems, this paper suggests a two-fold approach to improve the performance of the content-based recommender systems. The first approach is the refinement of the existing sparsity and inconsistencies in item features using matrix factorization. The second approach is the generation of individual preference profiles using iterative boosting of multiple weak learners for penalizing the misclassification of ratings. The suggested method is tested via benchmark recommender system datasets such as ML-1M, Last.fm, and Netflix. The results obtained during experiments show a significant improvement in recommendation quality over the state-of-the-art content-based recommender system models.

An easy and widely applicable forecast control for heating systems in existing and new buildings: First field experiences

An accurate prediction and regulation of the energy supplied to the buildings can increase the energy efficiency of existing and new buildings. However, there is no low cost, easy to install and operate system and a simple method for the forecast control of a heating system dedicated to existing buildings. This work elaborates the first experiences from the installation, operation and maintenance of the proposed forecast control system in a multi-family building and an office building located in Poland. The method takes into account the building's actual energy model and the installed heating system (in the form of an equivalent outdoor temperature), the user behavior and preference profiles (in the form of an equivalent indoor temperature) and the forecast of weather parameters. Actual energy savings resulting from the use of this forecast control system in the two monitored buildings averaged over 15%. The installation of this system in existing buildings requires less than 2 h and it does not impose any difficulties for users or facility managers.

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

ABSTRACTWhen travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.

Covering the Sensitive Subjects to Protect Personal Privacy in Personalized Recommendation

Personalized recommendation has demonstrated its effectiveness in improving the problem of information overload on the Internet. However, evidences show that due to the concerns of personal privacy, users’ reluctance to disclose their personal information has become a major barrier for the development of personalized recommendation. In this paper, we propose to generate a group of fake preference profiles, so as to cover up the user sensitive subjects, and thus protect user personal privacy in personalized recommendation. First, we present a client-based framework for user privacy protection, which requires not only no change to existing recommendation algorithms, but also no compromise to the recommendation accuracy. Second, based on the framework, we introduce a privacy protection model, which formulates the two requirements that ideal fake preference profiles should satisfy: (1) the similarity of feature distribution, which measures the effectiveness of fake preference profiles to hide a genuine user preference profile; and (2) the exposure degree of sensitive subjects, which measures the effectiveness of fake preference profiles to cover up the sensitive subjects. Finally, based on a subject repository of product classification, we present an implementation algorithm to well meet the privacy protection model. Both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach.

An Efficient Indexing Method for Skyline Computations with Partially Ordered Domains

Efficient processing of skyline queries with partially ordered domains has been intensively addressed in recent years. To further reduce the query processing time to support high-responsive applications, the skyline queries that were previously processed with user preferences similar to those of the new query contribute useful candidate result points. Hence, the answered queries can be cached with both their results and the user preferences such that the query processor can rapidly retrieve the result for a new query only from the result sets of cached queries with compatible user preferences. When caching a significant number of queries accumulated over time, it is essential to adopt effective access methods to index the cached queries to retrieve a set of relevant cached queries for facilitating the cache-based skyline query computations. In this paper, we propose an extended depth-first search indexing method (e-DFS for short) for accessing user preference profiles represented by directed acyclic graphs (DAGs), and emphasize the design of the e-DFS encoding that effectively encodes a user preference profile into a low-dimensional feature point which is eventually indexed by an R-tree. We obtain one or more traversal orders for each node in a DAG by traversing it through a modified version of the depth-first search which is utilized to examine the topology structure and dominance relations to measure closeness or similarity. As a result, e-DFS which combines the criteria of similarity evaluation is able to greatly reduce the search space by filtering out most of the irrelevant cached queries such that the query processor can avoid accessing the entire data set to compute the query results. Extensive experiments are presented to demonstrate the performance and utility of our indexing method, which outperforms the baseline planning techniques by reducing 37 percent of the computational time on average.

Personalized Information Gathering in a Multiple Personal Device Environment

We propose a personalized information gathering system PerIGS that exploits implicit user feedback in a multiple personal device environment. PerIGS extracts imposing words that induce user's choice from recommended news articles, and constructs user preference profile based on observed user behaviors. A user profile is shared by multiple personal devices, and PerIGS gathers, filters, and recommends news articles according to this shared profile. We organize the user profile in a hierarchical category structure to deal with the uncertainty of user interests. This structure also enables the system to track and recognize the changes of user interests for each domain more effectively. The results of a series of experiments measuring user satisfaction for recommended news articles indicated that PerIGS can effectively utilize user profiles for news filtering and recommendation.

A weighted adaptation method on learning user preference profile

Recommender systems typically store personal preference profiles. Many items in the profiles can be represented by numerical attributes. However, the initial profile of each user is incomplete and imprecise. One important problem in the development of these systems is how to learn user preferences, and how to automatically adapted update the profiles. To address this issue, this paper presents an unsupervised approach for learning user preferences over numeric attributes by analyzing the interactions between users and recommender systems. When a list of recommendations shown to a target user, the favorite item will be selected by him/her, then the selected item and the over-ranked items will be employed as valuable feedback to learn the user profile. Specifically, two contributions are offered: 1), a learning approach to measure the influence of over-ranked items through analysis of user feedbacks and 2), a weighting algorithm to calculate weights of different attributes by analyzing user selections. These two approaches are integrated into a traditional adaption model for updating user preference profile. Extensive simulations and results show that both approaches are more effective than existing approaches.

USER PREFERENCE PROFILE BASED CACHING FOR VIDEO STREAMING IN MOBILE NETWORKS

In radio access networks, mobile devices access internet content through base stations, which are connected to the internet via gateway routers. A number of studies have been carried out to improve the performance of internet access by mobile hosts. Among these, caching of popular web documents at locations close to the mobile clients is an effective method. The benefits of caching data in the core network of mobile carrier have been investigated in the literature. There has been some research in caching of videos for online video sites like YouTube, which aims at improving the performance of the mobile internet access networks. Caching of Most Popular Videos (MPV) together with LRU (Least Recently Used) - replacing the least recently used video when cache is full- algorithm is an efficient method for large sized caches which are only possible for internet content delivery networks. Caching algorithms for ad-hoc networks has also been developed. These are not applicable for radio access networks. This paper investigates the effectiveness of placing caches at base stations in cellular networks and considers the UPP (User Preference Profile) of each cell-site for caching of videos.

A Framework for Enabling User Preference Profiling through Wi-Fi Logs

Nowadays, mobile devices have become a ubiquitous medium supporting various forms of functionality and are widely accepted for commons. In this study, we investigate using Wi-Fi logs from a mobile device to discover user preferences. The core ideas are two folds. First, every Wi-Fi access point is with a network name, normally a human-readable string, called SSID (Service Set Identifier). Since SSIDs are often with semantics, from which we can infer the place where the user stayed. Second, a Wi-Fi log is produced when the user is near a Wi-Fi access point. A high frequency of a consecutively observed SSID implies a long stay duration at a place. To the best of our knowledge, our work is the first attempting to understand users from the collected Wi-Fi logs from mobile devices. However, Wi-Fi logs are essentially of various information types and with noises. How to assess the information types, eliminate irrelevant information, and clean up the noises within partial-informative SSIDs are therefore keys for profiling user preferences over Wi-Fi logs. In this paper, we propose a data cleaning and information enrichment framework for enabling the user preference understanding through collected Wi-Fi logs, and introduce a data clean framework for cleaning, correcting, and refining Wi-Fi logs. In addition, a comprehensive experiment with data collected from users is made to verify the effectiveness of the proposed techniques for cleaning noisy Wi-Fi data for user preferences profiling. The experiment results demonstrate the effectiveness of the proposed framework for profiling user preferences through Wi-Fi logs.

A Context-Aware Mobile User Behavior-Based Neighbor Finding Approach for Preference Profile Construction.

In this paper, a new approach is adopted to update the user preference profile by seeking users with similar interests based on the context obtainable for a mobile network instead of from desktop networks. The trust degree between mobile users is calculated by analyzing their behavior based on the context, and then the approximate neighbors are chosen by combining the similarity of the mobile user preference and the trust degree. The approach first considers the communication behaviors between mobile users, the mobile network services they use as well as the corresponding context information. Then a similarity degree of the preference between users is calculated with the evaluation score of a certain mobile web service provided by a mobile user. Finally, based on the time attenuation function, the users with similar preference are found, through which we can dynamically update the target user’s preference profile. Experiments are then conducted to test the effect of the context on the credibility among mobile users, the effect of time decay factors and trust degree thresholds. Simulation shows that the proposed approach outperforms two other methods in terms of Recall Ratio, Precision Ratio and Mean Absolute Error, because neither of them consider the context mobile information.

A Fuzzy Preference Tree-Based Recommender System for Personalized Business-to-Business E-Services

The Web creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems aim to automatically generate personalized suggestions of products/services to customers (businesses or individuals). Although recommender systems have been well studied, there are still two challenges in the development of a recommender system, particularly in real-world B2B e-services: (1) items or user profiles often present complicated tree structures in business applications, which cannot be handled by normal item similarity measures and (2) online users' preferences are often vague and fuzzy, and cannot be dealt with by existing recommendation methods. To handle both these challenges, this study first proposes a method for modeling fuzzy tree-structured user preferences, in which fuzzy set techniques are used to express user preferences. A recommendation approach to recommending tree-structured items is then developed. The key technique in this study is a comprehensive tree matching method, which can match two tree-structured data and identify their corresponding parts by considering all the information on tree structures, node attributes, and weights. Importantly, the proposed fuzzy preference tree-based recommendation approach is tested and validated using an Australian business dataset and the MovieLens dataset. Experimental results show that the proposed fuzzy tree-structured user preference profile reflects user preferences effectively and the recommendation approach demonstrates excellent performance for tree-structured items, especially in e-business applications. This study also applies the proposed recommendation approach to the development of a Web-based business partner recommender system.

A Multi-Agent MicroBlog Behavior based User Preference Profile Construction Approach

Nowadays, the user-centric application based web 2.0 has replaced the web 1.0. The users gain and provide information by interactive network applications. As a result, traditional approaches that only extract and analyze users’ local document operating behavior and network browsing behavior to build the users’ preference profile cannot fully reflect their interests. Therefore this paper proposed a preference analysis and indicating approach based on the users’ communication information from MicroBlog, such as reading, forwarding and @ behavior, and using the improved PersonalRank method to analyze the importance of a user to other users in the network and based on the users’ communication behavior to update the weight of the items in the user preference. Simulation result shows that our proposed method outperforms the ontology model, TREC model, and the category model in terms of 11SPR value.

A Context-Aware Mobile User Behavior Based Preference Neighbor Finding Approach for Personalized Information Retrieval

Abstract This paper adopts a new approach to searching the users with similarinterests by mobile network services instead of by desktop users. First, the direct trustis calculated based on the mobile users’ behavior andthe weight of the corresponding context (time and position), then an indirect trust degree calculation method is proposed according to the propagationdistance based on the theory of the six degrees of separation; secondly this paper further uses the evaluation score about a certain mobile web service given by a mobile user to calculate the preference similarity degree between different users. Finally based on the time attenuation functionit tries to find the users with similar preference,so as to make use of the neighbor users’ preference profile to dynamicallyupdate the target user's preference profile. Simulation shows that the proposed approach outperformspure sliding window methodand pure forgetting strategy method which fail totake the context mobile information into considerationin terms of Recall Ratio and Precision Ratio.

Video-Aware Scheduling and Caching in the Radio Access Network

In this paper, we introduce distributed caching of videos at the base stations of the Radio Access Network (RAN) to significantly improve the video capacity and user experience of mobile networks. To ensure effectiveness of the massively distributed but relatively small-sized RAN caches, unlike Internet content delivery networks (CDNs) that can store millions of videos in a relatively few large-sized caches, we propose RAN-aware reactive and proactive caching policies that utilize User Preference Profiles (UPPs) of active users in a cell. Furthermore, we propose video-aware backhaul and wireless channel scheduling techniques that, in conjunction with edge caching, ensure maximizing the number of concurrent video sessions that can be supported by the end-to-end network while satisfying their initial delay requirements and minimize stalling. To evaluate our proposed techniques, we developed a statistical simulation framework using MATLAB and performed extensive simulations under various cache sizes, video popularity and UPP distributions, user dynamics, and wireless channel conditions. Our simulation results show that RAN caches using UPP-based caching policies, together with video-aware backhaul scheduling, can improve capacity by 300% compared to having no RAN caches, and by more than 50% compared to RAN caches using conventional caching policies. The results also demonstrate that using UPP-based RAN caches can significantly improve the probability that video requests experience low initial delays. In networks where the wireless channel bandwidth may be constrained, application of our videoaware wireless channel scheduler results in significantly (up to 250%) higher video capacity with very low stalling probability.

A Weighted Method to Update Network User Preference Profile Dynamically

There are many alternatives in a Recommender System (RS) that can be represented by numerical attributes. One of the most challenging tasks in developing RS is the design of techniques that can infer user preferences through observation of their actions. A RS usually stores a personal preference profile associated with each user, but the initial profile of a user is usually incomplete and imprecise. Therefore, it is necessary to update a user's preference profile dynamically. Some previous research has covered this area, but neglected an important fact in real situations, where different weights should be considered for every attribute when selecting alternatives and updating a user preference profile. This paper provides a realistic and weighted method to update network user preferences through analysis of user selections. More specifically, an algorithm to compute and update weights of different attributes in a dynamic way is presented. The weights are used in the adaptation process of network user preference profile. The method is tested by extensive simulations and the obtained results show that it is more effective than previous methods.

개인화 서비스 기반 사용자 지향형 지리정보 검색 서비스 모듈 개발

최근의 지리정보서비스는 모바일 환경의 확장을 기반으로 점차 사용자의 관심, 취향을 고려한 개인화 지향형 서비스를 제공하는 단계로 발전하고 있다. 기존의 지리정보서비스는 범용적이고, 다수의 사용자를 위한 정보를 제공하는데 비해 개인화 지향형 서비스를 위한 지리정보서비스는 사용자의 관심지(POInt of Interest)를 중심으로 개인의 선호 정보, 관심지, 우선 순위 지정 등을 통해 구체적이고 부가적인 속성정보를 제공함으로써 사용자의 기호에 맞는 서비스를 제공할 수 있다. 본 논문에서는 개인화 지향형 지리정보 제공 서비스를 위한 지리정보 검색 서비스 모듈의 개발 내용을 기술한다. 제안하는 사용자 지향형 지리정보 검색 서비스는 사용자 선호 프로파일과 다중 네트워크를 통한 POI정보 획득, 이에 따른 서비스 플랫폼 구조와 구현 내용으로 구성되어 있다. 제안한 내용은 최적 주유소 탐색 서비스를 사례 연구로 구현하여 검증하였고, 구현된 서비스 모델은 음식점, 주유소, 편의점 등의 테마별 서비스에 확장 가능하다. Recently, GIS(Geographic Information System) has been developed to personalized service for providing the specialized services that is aimed to personal user based on mobile communication. The existing GIS system provides comprehensive and simple information but GIS System for personalized service must provide the adjustive information through the personal interest profile based on POI(PoInt of Interest). This paper describes the intelligent retrieval geographical information service module for providing personal oriented geographic information service. Our proposal model consists of user preference profile, acquisition of POI through hybrid network (Wireless LAN, CDMA), service platform and implementation of prototype system. Implementation model can apply to the life information service like restaurant, oil station, convenient store and etc.

Personalized Information Retrieval by Using Adaptive User Profiling and Collaborative Filtering

Many search engines such as Yahoo, Google, MSN, and AltaVista, have been developed to meet various users’ search needs in real world. In general, because of the lack of the personal information such as hobby, preferences, and interests, these existing information retrieval systems are unsuitable to provide personalized search results to users. In this paper, we propose an adaptive user profiling method using dynamic updating policy considering the change of the users’ preferences over time and domain. Moreover, we employ collaborative filtering method to handle the situation that users’ preferences are frequently or continuously changed. Experimental results show that our method considerably improved personalized search performance for each user through automatic creation, maintenance, and personalization of user preference profiles that include search patterns of individual users.

Emotion Sensitive News Agent (ESNA): A system for user centric emotion sensing from the news

News is an interesting application domain for “emotion sensing”, since readers often have a personal attitude or subjective opinion regarding certain events or entities reported about. Hence the ability to determine user-centric emotion on a given topic or entity is of critical interest. This paper describes a system called Emotion Sensitive News Agent (ESNA). By employing several RSS news feeds chosen by the user, ESNA has been developed as a news aggregator to fetch news, and to categorize the themes of the collected news into eight emotional affinities, thereby taking into consideration of the user's preference profile. A user study has been conducted, which indicates that the system is conceived as intelligent and interesting as an affective interface. ESNA exemplifies a recent research agenda that aims at recognizing affective information conveyed through texts. Different approaches have already been employed to “sense” emotion from text. The novelty of the approach mentioned here is threefold: affective information conveyed through text is analyzed (1) by using a rule based approach to assign a numerical valence (i.e., a positive or negative value to indicate positive or negative sentiment of the input) instead of a machine learning approach, (2) by considering the cognitive and appraisal structure of emotions, and (3) by taking into account user preferences.

Integrating device independence and user profiles on the Web

This paper describes research to enable appropriate knowledge to be automatically delivered to the right people at the right time at the right level of detail via a range of user devices (laptop PC, mobile telephone, wireless e-mail client, hand-held PC, etc). We describe a framework that integrates device profiles with user preference profiles and accessibility requirements to provide effective knowledge delivery to a range of end-user devices. The framework has two components — an ontology of device and user profiles, and a content adaptation engine for presenting information to the user in a suitable format.

Human factors in the design of a personalizable EPG: Preference-indication strategies, habit watching and trust

The article describes a user study to support the design of a personalizable EPG (Electronic TV Programme Guide), and of its user interface for editing user preference profiles regarding TV channels and categories. This study focuses on issues related to users' behaviour and perceptions regarding a personalizable EPG, and especially regarding the personalization process. Users were presented with a paper-and-pencil procedure to indicate their TV viewing preferences, as well as with an electronic version. Their strategies were observed and their opinions asked, especially on trusting a system that uses this data. Moreover, their viewing behaviour was monitored over a period of two weeks, and recommendations for the second week were based on the viewing behaviour of the first week. The results indicate that users are reasonably comfortable and consistent in describing their viewing habits in terms of preferences, both for the paper-and-pencil and electronic preference-indication procedures, but that fine tuning this profile on the basis of habit watching would considerably improve the efficacy of the recommendations. It was found that subjects trust the system with the task of preselecting their TV programmes on the basis of their preference indications, although they are not sure whether a habit-watching system would be capable of following their changing habits over time.