Using Semantic-Based User Profile Modeling for Context-Aware Personalised Place Recommendations

Place Recommendation Systems (PRS's) are used to recommend places to visit to World Wide Web users. Existing PRS's are still limited by several problems, some of which are the problem of recommending similar set of places to different users (Lack of Personalization) and no diversity in the set of recommended items (Content Overspecialization). One of the main objectives in the PRS's or Contextual suggestion systems is to fill the semantic gap among the queries and suggestions and going beyond keywords matching. To address these issues, in this study we attempt to build a personalized context-aware place recommender system using semantic-based user profile modeling to address the limitations of current user profile building techniques and to improve the retrieval performance of personalized place recommender system. This approach consists of building a place ontology based on the Open Directory Project (ODP), a hierarchical ontology scheme for organizing websites. We model a semantic user profile from the place concepts extracted from place ontology and weighted according to their semantic relatedness to user interests. The semantic user profile is then exploited to devise a personalized recommendation by re-ranking process of initial search results for improving retrieval performance. We evaluate this approach on dataset obtained using Google Paces API. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based place recommendation model

개인화 검색 및 파트너쉽 선정을 위한 사용자 프로파일링

학위논문 (박사)-- 서울대학교 대학원 : 치의과학과, 2014. 2. 김홍기.The secret of change is to focus all of your energy not on fighting the old, but on building the new. - Socrates The automatic identification of user intention is an important but highly challenging research problem whose solution can greatly benefit information systems. In this thesis, I look at the problem of identifying sources of user interests, extracting latent semantics from it, and modelling it as a user profile. I present algorithms that automatically infer user interests and extract hidden semantics from it, specifically aimed at improving personalized search. I also present a methodology to model user profile as a buyer profile or a seller profile, where the attributes of the profile are populated from a controlled vocabulary. The buyer profiles and seller profiles are used in partnership match. In the domain of personalized search, first, a novel method to construct a profile of user interests is proposed which is based on mining anchor text. Second, two methods are proposed to builder a user profile that gather terms from a folksonomy system where matrix factorization technique is explored to discover hidden relationship between them. The objective of the methods is to discover latent relationship between terms such that contextually, semantically, and syntactically related terms could be grouped together, thus disambiguating the context of term usage. The profile of user interests is also analysed to judge its clustering tendency and clustering accuracy. Extensive evaluation indicates that a profile of user interests, that can correctly or precisely disambiguate the context of user query, has a significant impact on the personalized search quality. In the domain of partnership match, an ontology termed as partnership ontology is proposed. The attributes or concepts, in the partnership ontology, are features representing context of work. It is used by users to lay down their requirements as buyer profiles or seller profiles. A semantic similarity measure is defined to compute a ranked list of matching seller profiles for a given buyer profile.1 Introduction 1 1.1 User Profiling for Personalized Search . . . . . . . . 9 1.1.1 Motivation . . . . . . . . . . . . . . . . . . . 10 1.1.2 Research Problems . . . . . . . . . . . . . . 11 1.2 User Profiling for Partnership Match . . . . . . . . 18 1.2.1 Motivation . . . . . . . . . . . . . . . . . . . 19 1.2.2 Research Problems . . . . . . . . . . . . . . 24 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . 25 1.4 System Architecture - Personalized Search . . . . . 29 1.5 System Architecture - Partnership Match . . . . . . 31 1.6 Organization of this Dissertation . . . . . . . . . . 32 2 Background 35 2.1 Introduction to Social Web . . . . . . . . . . . . . . 35 2.2 Matrix Decomposition Methods . . . . . . . . . . . 40 2.3 User Interest Profile For Personalized Web Search Non Folksonomy based . . . . . . . . . . . . . . . . 43 2.4 User Interest Profile for Personalized Web Search Folksonomy based . . . . . . . . . . . . . . . . . . . 45 2.5 Personalized Search . . . . . . . . . . . . . . . . . . 47 2.6 Partnership Match . . . . . . . . . . . . . . . . . . 52 3 Mining anchor text for building User Interest Profile: A non-folksonomy based personalized search 56 3.1 Exclusively Yours' . . . . . . . . . . . . . . . . . . . 59 3.1.1 Infer User Interests . . . . . . . . . . . . . . 61 3.1.2 Weight Computation . . . . . . . . . . . . . 64 3.1.3 Query Expansion . . . . . . . . . . . . . . . 67 3.2 Exclusively Yours' Algorithm . . . . . . . . . . . . 68 3.3 Experiments . . . . . . . . . . . . . . . . . . . . . . 71 3.3.1 DataSet . . . . . . . . . . . . . . . . . . . . 72 3.3.2 Evaluation Metrics . . . . . . . . . . . . . . 73 3.3.3 User Profile Efficacy . . . . . . . . . . . . . 74 3.3.4 Personalized vs. Non-Personalized Results . 76 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . 80 4 Matrix factorization for building Clustered User Interest Profile: A folksonomy based personalized search 82 4.1 Aggregating tags from user search history . . . . . 86 4.2 Latent Semantics in UIP . . . . . . . . . . . . . . . 90 4.2.1 Computing the tag-tag Similarity matrix . . 90 4.2.2 Tag Clustering to generate svdCUIP and modSvdCUIP 98 4.3 Personalized Search . . . . . . . . . . . . . . . . . . 101 4.4 Experimental Evaluation . . . . . . . . . . . . . . . 103 4.4.1 Data Set and Experiment Methodology . . . 103 4.4.1.1 Custom Data Set and Evaluation Metrics . . . . . . . . . . . . . . . 103 4.4.1.2 AOL Query Data Set and Evaluation Metrics . . . . . . . . . . . . . 107 4.4.1.3 Experiment set up to estimate the value of k and d . . . . . . . . . . 107 4.4.1.4 Experiment set up to compare the proposed approaches with other approaches . . . . . . . . . . . . . . . 109 4.4.2 Experiment Results . . . . . . . . . . . . . . 111 4.4.2.1 Clustering Tendency . . . . . . . . 111 4.4.2.2 Determining the value for dimension parameter, k, for the Custom Data Set . . . . . . . . . . . . . . . 113 4.4.2.3 Determining the value of distinctness parameter, d, for the Custom data set . . . . . . . . . . . . . . . 115 4.4.2.4 CUIP visualization . . . . . . . . . 117 4.4.2.5 Determining the value of the dimension reduction parameter k for the AOL data set. . . . . . . . . . . . 119 4.4.2.6 Determining the value of distinctness parameter, d, for the AOL data set . . . . . . . . . . . . . . . . . . 120 4.4.2.7 Time to generate svdCUIP and modSvd-CUIP . . . . . . . . . . . . . . . . 122 4.4.2.8 Comparison of the svdCUIP, modSvd-CUIP, and tfIdfCUIP for different classes of queries . . . . . . . . . . 123 4.4.2.9 Comparing all five methods - Improvement . . . . . . . . . . . . . . 124 4.4.3 Discussion . . . . . . . . . . . . . . . . . . . 126 5 User Profiling for Partnership Match 133 5.1 Supplier Selection . . . . . . . . . . . . . . . . . . . 137 5.2 Criteria for Partnership Establishment . . . . . . . 140 5.3 Partnership Ontology . . . . . . . . . . . . . . . . . 143 5.4 Case Study . . . . . . . . . . . . . . . . . . . . . . 147 5.4.1 Buyer Profile and Seller Profile . . . . . . . 153 5.4.2 Semantic Similarity Measure . . . . . . . . . 155 5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . 160 5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . 162 6 Conclusion 164 6.1 Future Work . . . . . . . . . . . . . . . . . . . . . . 167 6.1.1 Degree of Personalization . . . . . . . . . . . 167 6.1.2 Filter Bubble . . . . . . . . . . . . . . . . . 168 6.1.3 IPR issues in Partnership Match . . . . . . . 169 Bibliography 170 Appendices 193 .1 Pairs of Query and target URL . . . . . . . . . . . 194 .2 Examples of Expanded Queries . . . . . . . . . . . 197 .3 An example of svdCUIP, modSvdCUIP, tfIdfCUIP 198Docto

A paper recommender system based on user’s profile in big data scholarly

Users encounter a huge volume of papers in digital libraries and paper search engines such as IEEE Explore, ACM Digital library, Google scholar and etc. these high number of papers make some difficulties for researchers for finding proper information and items. Recommender systems contain successful tools for knowledge of users and identification of their priorities. These systems present a personalized proposal to users who seek to find a special kind of relevant data or their priorities through the big number of data. Recommendersystem based on personalization uses the user profile and in view of the fact that the user profile encompass information pertaining to the user priorities; so it is a very active district in data recovery. Recommendersystem is an attitude that presented in order to encounter difficulties caused by abundant data and it helps users to attain their goals quickly through huge number of data. In this paper, we have presented an approach that received entire of available information in a paper, and formed a profile for each user by short and long inquiries; this profile is personalized for user and then recommends the closest paperto the  user by the user profile. Findings indicate that suggested approach outperformsthe similar approaches.Keywords: recommender system; bigdata; user profile; content-based recommender system; hadoo

K-Nearest neighbor algorithm on implicit feedback to determine SOP

The availability of a lot of existing Standard Operating Procedures (SOP) document information, users often need time to find SOPs that fit their preference. Therefore, this requires a recommendation system based on user content consumption by personalized usage logs to support the establishment of SOP documents managed according to user preferences. The k-nearest neighbor (KNN) algorithm is used to identify the most relevant SOP document for the user by utilizing implicit feedback based on extraction data by monitoring the document search behavior. From the research results obtained 5 classifications as parameters, with a final value of 3:2 ratio that shows the best distance value with the majority of labels according to the concept of calculation KNN algorithm that sees from the nearest neighbor in the dataset. This shows the precision of applying the KNN algorithm in determining SOP documents according to user preferences based on implicit feedback resulting in 80% presentation for SOPs corresponding to profiles and 20% for SOPs that do not fit the user profile. To establish SOP documents to show more accurate results, it should be used in a broad SOP management system and utilize implicit feedback with parameters not only in search logs and more on performance evaluation evaluations

Improved Reinforcement-Based Profile Learning For Document Filtering

Today the amount of accessible information is overwhelming. A personalized information filtering system must be able to tailor to current interests of the user and to adapt as they change over time. This system has to monitor a stream of incoming documents to learn the user’s information requirements, which is the user profile. The research has proposed a content-based personal information system learns the user’s preferences by analyzing the document contents and building a user profile. This system is called RePLS; an agent-based Reinforcement Profile Learning System with adaptive information filtering. The research focuses on an improved terms weighting to measure the importance of the terms represent each profile called “purity term weighting”. The top selected terms are then used to filter the incoming documents to the learned user profiles. The agent approach is used because of its autonomous and adaptive capabilities to perform the filtering. The proposed method was evaluated and compared with three Information Filtering methods, namely Rocchio, Okapi/BSS Basic Search System and Reinf, the incremental profile learning method. Based on the proposed method, a profile learning system is developed using Microsoft VC++ connected to Microsoft Access database through an ODBC. AFC kit is used to implement the proposed agents under RETSINA architecture. The experiments are carried out on the TREC 2002 Filtering Track dataset provided by the National Institute of Standards and Technology (NIST). This research has proven that RePLS is able to filter the stream of incoming documents according to the user interests (profiles) learned by the proposed Purity term weighting method. Based on the experiments results, Purity weighting shows better terms weighting and profile learning than the other methods. The outcome of a considerably good accuracy is mainly due to the right weighting of the profile’s terms during the learning phase. This research opens a wide range of future works to be considered, including the investigation of the dependency between the selected terms for each profile, investigating the quality of the method on different datasets, and finally, the possibility to apply the proposed method in other area like the recommendation systems

Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization

In this paper we study the personalized text search problem. The keyword based search method in conventional algorithms has a low efficiency in understanding users' intention since the semantic meaning, user profile, user interests are not always considered. Firstly, we propose a novel text search algorithm using a inverse filtering mechanism that is very efficient for label based item search. Secondly, we adopt the Bayesian network to implement the user interest prediction for an improved personalized search. According to user input, it searches the related items using keyword information, predicted user interest. Thirdly, the word vectorization is used to discover potential targets according to the semantic meaning. Experimental results show that the proposed search engine has an improved efficiency and accuracy and it can operate on embedded devices with very limited computational resources

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201