abellogin's personalisation [80 articles]

Abstract

We study personalized recommendation of social software items, including bookmarked web-pages, blog entries, and communities. We focus on recommendations that are derived from the user's social network. Social network information is collected and aggregated across different data sources within our organization. At the core of our research is a comparison between recommendations that are based on the user's familiarity network and his/her similarity network. We also examine the effect of adding explanations to each recommended item that show related people and ...

Abstract

Social tagging is becoming increasingly popular in many Web 2.0 applications where users can annotate resources (e.g. Web pages) with arbitrary keywords (i.e. tags). A tag recommendation module can assist users in tagging process by suggesting relevant tags to them. It can also be directly used to expand the set of tags annotating a resource. The benefits are twofold: improving user experience and enriching the index of resources. However, the former one is not emphasized in previous studies, though a lot ...

Abstract

Web search engines typically provide search results without considering user interests or context. We propose a personalized search approach that can easily extend a conventional search engine on the client side. Our mapping framework automatically maps a set of known user interests onto a group of categories in the Open Directory Project (ODP) and takes advantage of manually edited data available in ODP for training text classifiers that correspond to, and therefore categorize and personalize search results according to user interests. ...

Abstract

The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications have been done before. In this paper we study how important the contextual information is when predicting customer behavior and how to use it when building customer models. It is done by conducting an empirical study across a wide range of experimental ...

Abstract

In this paper, we introduce a novel approach for modelling user interests. Our approach captures users’ evolving information needs, identifies aspects of their need and recommends relevant news items to the users. We introduce our approach within the context of personalised news video retrieval. A news video data set is used for experimentation. We employ a simulated user evaluation. ...

Abstract

Three linked qualitative studies were performed to investigate why people choose to personalize the appearance of their PCs and mobile phones and what effects personalization has on their subsequent perception of those devices. The 1st study involved 35 frequent Internet users in a 2-stage procedure. In the 1st phase they were taught to personalize a commercial Web portal and then a recommendation system, both of which they used in the subsequent few days. In the 2nd phase they were allocated to ...

Abstract

News@hand is a news recommender system that makes use of semantic technologies to provide several on-line news recommendation services. News contents and user preferences are described in terms of concepts appearing in a set of domain ontologies. Based on the similarities between item descriptions and user profiles, and the se-mantic relations between concepts, content-based and collaborative recommendation models are supported by the system. In this paper, we evaluate a model that personalizes the order in which news articles are shown to ...

Abstract

Personality psychology explores personal determinants of social behavior, that is, psychological systems that causally contribute to the coherent patterns of experience and action that distinguish individuals from one another. This article explores two obstacles faced by evolutionary accounts of personality functioning. The first is the problem of act identification. Explaining social behavior by reference to an evolved mental module requires that one determine which module to invoke. This generally requires identifying the meaning of complex, culturally and socially embedded actions. Evolutionary ...

Note (first note only)

Prediction and explanation would require assessment of the relative strengths of activation of the multiple modules. This is commonly not possible.

The explanation is interactionist (Buss, 1996, p. 9), at least in a limited sense, in that the mechanisms interact with social contexts to generate the behavior. Environmental inputs activate a given module that then generates an evolved strategy. The analysis of the cheating detection module is a classic case (Cosmides, 1989). The mechanism contains procedures that recognize the possibility of

Abstract

Retrieving relevant items as a response to a user query is the aim of each information retrieval system. But ‘without an understanding of what relevance means to users, it is difficult to imagine how a system can retrieve relevant information for users’ [1]. In this paper, we try to capture what relevance is for a particular user and model his profile implicitly considering his non declared preferences that are inferred from a ranking of a reduced set of retrieved documents that ...

Abstract

Web personalization is the process of customizing a web site to the needs of each specific user or set of users. Personalization of a web site may be performed by the provision of recommendations to the users, high-lighting/adding links, creation of index pages, etc. The web personalization systems are mainly based on the exploitation of the navigational patterns of the web site’s visitors. When a personalization system relies solely on usage-based results, however, valuable information conceptually related to what is finally ...

Abstract

The success of the Semantic Web depends on the availability of Web pages annotated with metadata. Free form metadata or tags, as used in social bookmarking and folksonomies, have become more and more popular and successful. Such tags are relevant keywords associated with or assigned to a piece of information (e.g., a Web page), describing the item and enabling keyword-based classification. In this paper we propose P-TAG, a method which automatically generates personalized tags for Web pages. Upon browsing a Web ...

Abstract

There are many kinds of personalizing approaches in the area of web information retrieval. But it is still unclear whether personalization is consistently effective on different queries for different users, and under different search contexts. In this paper, we study this problem and propose a personalized search approach that can easily extend a conventional search engine. We present an intelligent relevance-evaluation framework for user Intention-based personalized search based on web-mining and machine learning approaches. Users can navigate through the prior user's ...

Abstract

Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to tailor search results to a particular user based on that user's interests and preferences. Effective personalization of information access involves two important challenges: accurately identifying the user context and organizing the information in such a way that matches the particular context. We present an approach to personalized search that involves building models of user context as ...

Abstract

Although personalized search has been under way for many years and many personalization algorithms have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users and under different search contexts. In this paper, we study this problem and provide some findings. We present a large-scale evaluation framework for personalized search based on query logs and then evaluate five personalized search algorithms (including two click-based ones and three topical-interest-based ones) using 12-day query logs of ...

Note (first note only)

It is still unclear whether personalization is consistently effective on different queries for different users and under different search contexts. In this paper, we study this problem and provide some findings.

One problem of current personalized search is that most proposed algorithms are uniformly applied to all users and queries. We argue that queries should not be handled in the same general manner: personalization may lack effectiveness on some queries (topical-interest-based, using history, context).

Experimental results show that personalization brings significant search accuracy

Abstract

Collaborative tagging systems, sometimes referred to as "folksonomies", enable Internet users to annotate or search for resources using custom labels instead of being restricted by pre-defined navigational or conceptual hierarchies. However, the flexibility of tagging brings with it certain costs. Because users are free to apply any tag to any resource, tagging systems contain large numbers of redundant, ambiguous, and idiosyncratic tags which can render resource discovery difficult. Data mining techniques such as clustering can be used to ameliorate this problem ...

Abstract

Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and ...

Abstract

Alice wants to query a database but she does not want the database to learn what she is querying. She can ask for the entire database. Can she get her query answered with less communication? One model of this problem is Private Information Retrieval, henceforth PIR. We survey results obtained about the PIR model including partial answers to the following questions. (1) What if there are k non-communicating copies of the database but they are computationally ...

Abstract

With the expansion of digital networks and TV devices and the rapid increase of the number of channels, people are exposed to an information overload, due to the presence of several hundreds of alternative programs to watch. In this context, personalization is achieved with the employment of algorithms and data collection schemes that predict and recommend to television viewers content that match their interests and/or needs. This paper introduces queveo.tv: a personalized TV program recommendation system. The proposed hybrid approach (combining ...

Abstract

Traditional personalized search approaches rely solely on individual profiles to construct a user model. They are often confronted by two major problems: data sparseness and cold-start for new individuals. Data sparseness refers to the fact that most users only visit a small portion of Web pages and hence a very sparse user-term relationship matrix is generated, while cold-start for new individuals means that the system cannot conduct any personalization without previous browsing history. Recently, community-based approaches were proposed to use the ...

Abstract

Multiple-dimensional, i.e., polyadic, data exist in many applications, such as personalized recommendation and multiple-dimensional data summarization. Analyzing all the dimensions of polyadic data in a principled way is a challenging research problem. Most existing methods separately analyze the marginal relationships among pairwise dimensions and then combine the results afterwards. Motivated by the fact that various dimensions of polyadic data jointly affect each other, we propose a probabilistic polyadic factorization approach to directly model all the dimensions simultaneously in a unified framework. ...

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We relate the problem of information retrieval and that of collaborative filtering at a conceptual level. Yet, at the modelling level they are quite apart from each other, as their input data and purposes are completely different. Consequently, applying the information retrieval (relevance) models to collaborative filtering is not trivial. The difficulty lies in the fact that in text retrieval both queries and documents are represented by texts, which provide an important information channel to link queries (user needs) and documents.

Abstract

Abstract  Collaborative filtering is concerned with making recommendations about items to users. Most formulations of the problem are specifically designed for predicting user ratings, assuming past data of explicit user ratings is available. However, in practice we may only have implicit evidence of user preference; and furthermore, a better view of the task is of generating a top-N list of items that the user is most likely to like. In this regard, we argue that collaborative filtering can be directly cast as ...

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Explicit ratings are hard to gather in a real system (Claypool et al. 2001). It is highly desirable to infer user preferences from implicit observations of user interactions with a system. Academic research into frequency-counted user profiles for collaborative filtering has been limited. A large body of research work for collaborative filtering by default focuses on rating-based user profiles (Adomavicius and Tuzhilin et al. 2005; Marlin 2004). They are specifically designed for rating prediction, making them difficult to apply in many

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Expert finding is the task of discovering Who knows what among the employees of an organization. Recent work on automatic expert finders has formulated the problem of determiningwho has knowledge in a particular area as a retrieval task to rank people given a query topic. However, a standard retrieval system cannot solve this problem directly.

There are two principal approaches to expert modeling: query-dependent and query-independent. In both cases the expert system has to discover documents (ormore generally, snippets of text) related

Abstract

This paper introduces a new memory based approach to ratings based collaborative filtering. Unlike existing memory based collaborative filtering approaches, this approach exploits the predictable portions of even some complex relationships between users while selecting the mentors for an active user through the use of the novel notion of selective predictability, which can be measured using the Entropy measure. The proposed approach has been tested using the MovieLens dataset, and it is expected that this approach should work equally well for ...

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Several categories of Recommenders have been identified based on the filtering mechanism used to meet this objective. The three main categories include Content based Filtering, Collaborative Filtering and Hybrid Filtering [2]. Algorithms for Collaborative Filtering have been grouped into two general classes: memory-based (or heuristic-based) algorithms and model-based algorithms [5]. A new graph theoretic approach to Collaborative Filtering was proposed in [3] wherein the notion of Predictability was introduced in the place of Similarity.

The main contribution of this paper lies in

Abstract

Based on the brief introduction to the user-based and item-based collaborative filtering algorithms, the problems related to the two algorithms are analyzed, and a new entropy-based recommendation algorithm is proposed. Aimed at the drawbacks of traditional similarity measurement methods, we put forward an improved similarity measurement method. The entropy-based collaborative filtering algorithm contributes to solving the cold-start problem and discovering users'hidden interests. Using the practical data obtained from Movielens website and MAE metrics for accuracy measure, three different collaborative filtering recommendation ...

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Collaborative filtering is the most widely used technology in personalized recommender systems. Typically it can be classified into user-based and item-based collaborative filtering approaches.

User-based: provide item recommendations or predictions to the target user based on the opinions of other like-minded users [13]. The target user's rating for an item can be predicted by combining the ratings of his nearest-neighbors [10],[14]. There are many kinds of similarity measurement methods. To a great extent, the efficiency and effectiveness of user-based collaborative filtering

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We propose a formal framework for expressing and combining user preferences to address this problem. Preferences can be used to focus search queries and to order the search results. A preference is expressed by the user for an entity which is described by a set of named fields; each field can take on values from a certain type. The * symbol may be used to match any element of that type. A set of preferences can be combined using a generic

Abstract

This paper studies rank aggregation by using ontology-based user preferences in the context of Web search. We introduce a set of techniques to combine the respective rank lists produced by different attributes of user preferences. Furthermore, the learned user preferences are structured as a taxonomic hierarchy (a simple ontology). We use the learned ontology to store the attributes such as, the topics that a user is interested in and the degrees of user interests in these topics. The primary goal of ...

Note (first note only)

Different users have different intentions for a same query. In order to satisfy the diverse needs of users, search engines should be adaptive to the individual contexts in which users submit their queries. User preferences are a kind of useful contexts.

To leverage the rankings produced by the different attributes, rank aggregation intends to form a single rank list supported by a broad consensus among these attributes. In this paper we introduce methods to effectively improve the Web search in a context-aware

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To investigate how query ambiguity affects result quality, we conducted a study to examine the consistency of relevance judgments assigned by different individuals to the results of the same query. Rather than instructing participants to select results that were \textit{relevant to the query} in general, we asked them to indicate results that were \textit{personally relevant to them}, (i.e., what they meant by the query). We then quantified the variability in information goals associated with the same query.

We conducted a study in

Abstract

We present News@hand, a news recommender system which applies semantic-based technologies to describe and relate news contents and user preferences in order to produce enhanced recommendations. The exploitation of conceptual information describing contents and user profiles, along with the capability of inferring knowledge from the semantic relations defined in the ontologies, enabling different content-based collaborative recommendation models, are the key distinctive aspects of the system. The multi-domain portability, the multi-media source applicability, and addressing of some limitations of current recommender systems ...

Abstract

Personalized search is a promising way to better serve different users' information needs. Search history is one of the major information sources for search personalization. We investigated the impact of history length on the effectiveness of personalized ranking. We carried out task-based user study for Web search, and obtained ranked relevance judgments for all queries. Query contexts derived from previous queries in the same task are used to re-rank results for the current query. Experimental results show that the performance of ...

Abstract

In most previous work on personalized search algorithms, the results for all queries are personalized in the same manner. However, as we show in this paper, there is a lot of variation across queries in the benefits that can be achieved through personalization. For some queries, everyone who issues the query is looking for the same thing. For other queries, different people want very different results even though they express their need in the same way. We examine variability in user ...

Note (first note only)

There is a lot of variation across queries in the benefits that can be achieved through personalization: for some queries, everyone who issues the query is looking for the same thing; for other queries, different people want very different results even though they express their need in the same way. While variation in user behavior is correlated with variation in explicit relevance judgments the same query, there are many other factors, such as result entropy, result quality, and task that can

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Recommender system is one such promising technology that aims to generate item recommendations from a huge collection of items based on users' preferences.

Collaborative filtering is usually adopted in two classes of application scenarios[2]: a user is presented with one individual item at a time along with a predicted rating indicating the user's potential interest in the item; the second class of applications produce an ordered list of Top-N recommended items where the highest ranked items are predicted to be most preferred

Abstract

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of ...