Personalized web search using clickthrough data and web page rating

Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to construct personalized information retrieval model from the users' clickthrough data and Web page ratings. This model builds on the userbased collaborative filtering technology and the top-N resource recommending algorithm, which consists of three parts: user profile, user-based collaborative filtering, and the personalized search model. Firstly, we conduct user's preference score to construct the user profile from clicked sequence score and Web page rating. Then it attains similar users with a given user by user-based collaborative filtering algorithm and calculates the recommendable Web page scoring value. Finally, personalized informaion retrieval be modeled by three case applies (rating information for the user himself; at least rating information by similar users; not make use of any rating information). Experimental results indicate that our technique significantly improves the search performance. © 2012 ACADEMY PUBLISHER

Relevance feedback for best match term weighting algorithms in information retrieval

Personalisation in full text retrieval or full text filtering implies reweighting of the query terms based on some explicit or implicit feedback from the user. Relevance feedback inputs the user's judgements on previously retrieved documents to construct a personalised query or user profile. This paper studies relevance feedback within two probabilistic models of information retrieval: the first based on statistical language models and the second based on the binary independence probabilistic model. The paper shows the resemblance of the approaches to relevance feedback of these models, introduces new approaches to relevance feedback for both models, and evaluates the new relevance feedback algorithms on the TREC collection. The paper shows that there are no significant differences between simple and sophisticated approaches to relevance feedback

Probabilistic learning for selective dissemination of information

New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of information retrieval. The model is based on the concept of 'uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile

A model for mobile content filtering on non-interactive recommendation systems

To overcome the problem of information overloading in mobile communication, a recommendation system can be used to help mobile device users. However, there are problems relating to sparsity of information from a first-time user in regard to initial rating of the content and the retrieval of relevant items. In order for the user to experience personalized content delivery via the mobile recommendation system, content filtering is necessary. This paper proposes an integrated method by using classification and association rule techniques for extracting knowledge from mobile content in a user's profile. The knowledge can be used to establish a model for new users and first rater on mobile content. The model recommends relevant content in the early stage during the connection based on the user's profile. The proposed method also facilitates association to be generated to link the first rater items to the top items identified from the outcomes of the classification and clustering processes. This can address the problem of sparsity in initial rating and new user's connection for non-interactive recommendation systems

A Contextual Information Retrieval Model based on Influence Diagrams

International audienceA key challenge in information retrieval is the use of contex- tual evidence within the ad-hoc retrieval. Our contribution is particularly based on the belief that contextual retrieval is a decision-making prob- lem. For this reason we propose to apply influence diagrams witch are an extension of Bayesian networks to such problems, in order to solve the hard problem of user based relevance estimation. The basic underlying idea is to substitute to the traditional relevance function which measures the degree of matching document-query, a function indexed by the user. In our approach, the user profile is represented by his long term interests. In order to validate our model, we propose furthermore a novel evaluation protocol suitable for the contextual retrieval task. The test collection is an expansion of the standard TREC test data, obtained using a learning scenario of the user's interests. The experimental results show that our model is promising

Developing an Ontological approach to Content-based Recommendation System

During past decade the no of web user increases so rapidly leading to rapid increase in web services which leads to increase the usage data at higher rate. The usage data of these users now amount to be in order of Peta Byte (1015 bytes). In such cases the search space for user‟s queries increases and a user‟s search query may leads to retrieval of irrelevant information. Sometime the search algorithm may become exhaustive. This project is aimed to use User‟s context information to model a framework which can filter the search space and choose some preference based on user‟s context. The project follows a set of processes for profile construction of Users and Items, and determining their similarity and scoring their preference. The projects also compares the effectiveness in predicting User‟s preferences and accuracy in it with various other Collaborative approach such as User-Based model and Item based model to check its performance level and quality of predictions

Fuzzy rule based profiling approach for enterprise information seeking and retrieval

With the exponential growth of information available on the Internet and various organisational intranets there is a need for profile based information seeking and retrieval (IS&R) systems. These systems should be able to support users with their context-aware information needs. This paper presents a new approach for enterprise IS&R systems using fuzzy logic to develop task, user and document profiles to model user information seeking behaviour. Relevance feedback was captured from real users engaged in IS&R tasks. The feedback was used to develop a linear regression model for predicting document relevancy based on implicit relevance indicators. Fuzzy relevance profiles were created using Term Frequency and Inverse Document Frequency (TF/IDF) analysis for the successful user queries. Fuzzy rule based summarisation was used to integrate the three profiles into a unified index reflecting the semantic weight of the query terms related to the task, user and document. The unified index was used to select the most relevant documents and experts related to the query topic. The overall performance of the system was evaluated based on standard precision and recall metrics which show significant improvements in retrieving relevant documents in response to user queries

Keyword based profile creation using latent dirichlet allocation, domain dictionary and domain ontology / Nor Adzlan Jamaludin

Expert Finding is a field in information retrieval that focuses on finding an expert based on several criteria. Some of the methods that have been applied for expert finding include statistical, machine learning and ontology-based methods. Profile creation is one of the steps or tasks that are required in expert finding, which is the process of capturing and representing the details of experts and users which later can be used for retrieval. An issue that is faced for profile creation in expert finding is that the profiles being created are focused on the details of the experts but not on the users who are searching for these experts. This research explores a profile creation model that creates domain specific keyword-based profiles of users using Latent Dirichlet Allocation, domain dictionary and domain ontology from bookmarks. The domain of agriculture is selected as the case study for this research. The model is implemented in a form of a prototype and is evaluated by comparing how similar the prototype created profiles with manually built ones. From the results and analysis of the research, it is concluded that the method can successfully create domain specific profiles. The significances and contributions of the research include the application of LDA in user profiling, the proposed model, model prototype and the results and findings of the experiments conducted throughout the research

Generalized Group Profiling for Content Customization

There is an ongoing debate on personalization, adapting results to the unique user exploiting a user's personal history, versus customization, adapting results to a group profile sharing one or more characteristics with the user at hand. Personal profiles are often sparse, due to cold start problems and the fact that users typically search for new items or information, necessitating to back-off to customization, but group profiles often suffer from accidental features brought in by the unique individual contributing to the group. In this paper we propose a generalized group profiling approach that teases apart the exact contribution of the individual user level and the "abstract" group level by extracting a latent model that captures all, and only, the essential features of the whole group. Our main findings are the followings. First, we propose an efficient way of group profiling which implicitly eliminates the general and specific features from users' models in a group and takes out the abstract model representing the whole group. Second, we employ the resulting models in the task of contextual suggestion. We analyse different grouping criteria and we find that group-based suggestions improve the customization. Third, we see that the granularity of groups affects the quality of group profiling. We observe that grouping approach should compromise between the level of customization and groups' size.Comment: Short paper (4 pages) published in proceedings of ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR'16