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Visible relations in online communities : modeling and using social networksWebster, Andrew 21 September 2007 (has links)
The Internet represents a unique opportunity for people to interact with each other across time and space, and online communities have existed long before the Internet's solidification in everyday living. There are two inherent challenges that online communities continue to contend with: motivating participation and organizing information. An online community's success or failure rests on the content generated by its users. Specifically, users need to continually participate by contributing new content and organizing existing content for others to be attracted and retained. I propose both participation and organization can be enhanced if users have an explicit awareness of the implicit social network which results from their online interactions. My approach makes this normally ``hidden" social network visible and shows users that these intangible relations have an impact on satisfying their information needs and vice versa. That is, users can more readily situate their information needs within social processes, understanding that the value of information they receive and give is influenced and has influence on the mostly incidental relations they have formed with others. First, I describe how to model a social network within an online discussion forum and visualize the subsequent relationships in a way that motivates participation. Second, I show that social networks can also be modeled to generate recommendations of information items and that, through an interactive visualization, users can make direct adjustments to the model in order to improve their personal recommendations. I conclude that these modeling and visualization techniques are beneficial to online communities as their social capital is enhanced by "weaving" users more tightly together.
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Personalized Document Recommendation by Latent Dirichlet AllocationChen, Li-Zen 13 August 2012 (has links)
Accompanying with the rapid growth of Internet, people around the world can easily distribute, browse, and share as much information as possible through the Internet. The enormous amount of information, however, causes the information overload problem that is beyond users¡¦ limited information processing ability. Therefore, recommender systems arise to help users to look for useful information when they cannot describe the requirements precisely.
The filtering techniques in recommender systems can be divided into content-based filtering (CBF) and collaborative filtering (CF). Although CF is shown to be superior over CBF in literature, personalized document recommendation relies more on CBF simply because of its text content in nature. Nevertheless, document recommendation task provides a good chance to integrate both techniques into a hybrid one, and enhance the overall recommendation performance.
The objective of this research is thus to propose a hybrid filtering approach for personalized document recommendation. Particularly, latent Dirichlet allocation to uncover latent semantic structure in documents is incorporated to help us to either obtain robust document similarity in CF, or explore user profiles in CBF. Two experiments are conducted accordingly. The results show that our proposed approach outperforms other counterparts on the recommendation performance, which justifies the feasibility of our proposed approach in real applications.
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A context-aware system to predict user's intention on smartphone based on ECA ModelLee, Ko-han 21 August 2012 (has links)
With the development of artificial intelligence , the application of recommender systems has been extended to fields such as e-commerce shopping cart analysis or video recommendation system. These systems provide user a recommended resource set based on their habits or behavior patterns to help users saving searching cost. However, these techniques have not been successfully adopted to help users search functions on smart-phones more efficiency. This research is designated to build the context-aware system, which can generate the list of operations predicting which function user might use under certain contexts through continuously learning users operation patterns and related device perceived scenario. The system utilize event-condition-action patterns to describe user frequent behaviors, and the research will focus on developing innovative Action-Condition-Fit algorithm to figure the similarity between action pattern sets and real-time scenario. Proposed system and algorithm will then be built on Google App Engine and Android device to empirically validate its performance through field test.
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Probabilistic Latent Semantic Analysis Based Framework For Hybrid Social Recommender SystemsEryol, Erkin 01 June 2010 (has links) (PDF)
Today, there are user annotated internet sites, user interaction logs, online user communities which are valuable sources of information concerning the personalized recommendation problem. In the literature, hybrid social recommender systems have been proposed to reduce the sparsity of the usage data by integrating the user related information sources together. In this thesis, a method based on probabilistic latent semantic analysis is used as a framework for a hybrid social recommendation system. Different data hybridization approaches on probabilistic latent semantic analysis are experimented. Based on this flexible probabilistic model, network regularization and model blending approaches are applied on probabilistic latent semantic analysis model as a solution for social trust network usage throughout the collaborative filtering
process. The proposed model has outperformed the baseline methods in our experiments. As a result of the research, it is shown that the proposed methods successfully model the rating and social trust data together in a theoretically principled
way.
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A Hybrid Recommendation System Capturing The Effect Of Time And Demographic DataOktay, Fulya 01 June 2010 (has links) (PDF)
The information that World Wide Web (WWW) provides have grown up very rapidly in recent years, which resulted in new approaches for people to reach the information they need. Although web pages and search engines are indeed strong enough for us to reach what we want, it is not an efficient solution to present data and wait people to reach it. Some more creative and beneficial methods had to be developed for decreasing the time to reach the information and increase the quality of the information. Recommendation systems are one of the ways for achieving this purpose. The idea is to design a system that understands the information user wants to obtain from user actions, and to find the information similar to that. Several studies have been done in this field in order to develop a recommendation system which is capable of recommending movies, books, web sites and similar items like that. All of them are based on two main principles, which are collaborative filtering and content based recommendations. Within this thesis work, a recommendation system approach which combines both content based (CB) and collaborative filtering (CF) approaches by capturing the effect of time like purchase time or release time. In addition to this temporal behavior, the influence of demographic information of user on purchasing habits is also examined this system which is called &ldquo / TDRS&rdquo / .
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Probabilistic Matrix Factorization Based Collaborative Filtering With Implicit Trust Derived From Review Ratings InformationErcan, Eda 01 September 2010 (has links) (PDF)
Recommender systems aim to suggest relevant items that are likely to be of interest to the users using a variety of information resources such as user pro
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Borgo: Book Recommender For Reading GroupsDuzgun, Sayil 01 February 2012 (has links) (PDF)
With the increasing amount of data on web, people start to need tools which will help
them to deal with the most significant ones among the thousands. The idea of a
system which recommends items to its users emerged to fulfill this inevitable need.
But most of the recommender systems make recommendations for individuals. On
the other hand, some people need recommendation for items which they will use or
for activities which they will attend together. Group recommenders serve for these
purposes. Group recommenders diverge from individual recommenders such that
they need to aggregate members of the group in a joint model, and in order to do so,
they need a user satisfaction function. There are two different aggregation methods
and a few different satisfaction functions for group recommendation process. Reading
groups domain is a new domain for group recommenders. In this thesis we propose a
web based group recommender system which is called BoRGo: Book Recommender
for Reading Groups , for reading groups domain. BoRGo uses a new information
filtering technique and present a media for post recommendation processes. We
present comparative evaluation results of this new technique in this thesis.
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Item-level Trust-based Collaborative Filtering Approach to Recommender SystemsLu, Chia-Ju 23 July 2008 (has links)
With the rapid growth of Internet, more and more information is disseminated in the World Wide Web. It is therefore not an easy task to acquire desired information from the Web environment due to the information overload problem. To overcome this difficulty, two major methods, information retrieval and information filtering, arise. Recommender systems that employ information filtering techniques also emerge when the users¡¦ requirements are too vague in mind to express explicitly as keywords.
Collaborative filtering (CF) refers to compare novel information with common interests shared by a group of people for recommendation purpose. But CF has major problem: sparsity. This problem refers to the situation that the coverage of ratings appears very sparse. With few data available, the user similarity employed in CF becomes unstable and thus unreliable in the recommendation process. Recently, several collaborative filtering variations arise to tackle the sparsity problem. One of them refers to the item-based CF as opposed to the traditional user-based CF. This approach focuses on the correlations of items based on users¡¦ co-rating. Another popular variation is the trust-based CF. In such an approach, a second component, trust, is taken into account and employed in the recommendation process.
The objective of this research is thus to propose a hybrid approach that takes both advantages into account for better performance. We propose the item-level trust-based collaborative filtering (ITBCF) approach to alleviate the sparsity problem. We observe that ITBCF outperforms TBCF in every situation we consider. It therefore confirms our conjecture that the item-level trusts that consider neighbors can stabilize derived trust values, and thus improve the performance.
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Applications of Agent Based Approaches in Business: A Three Essay DissertationPrawesh, Shankar 01 January 2013 (has links)
The goal of this dissertation is to investigate the enabling role that agent based simulation plays in business and policy. The aforementioned issue has been addressed in this dissertation through three distinct, but related essays. The first essay is a literature review of different research applications of agent based simulation in various business disciplines, such as finance, economics, information systems, management, marketing and accounting. Various agent based simulation tools to develop computational models are discussed. The second essay uses an agent-based simulation approach to study important properties of the widely used most popular news recommender systems (NRS). This essay highlights the major limitations of most popular NRS in terms of: (i) susceptibility towards manipulation and (ii) unduly penalizing the article which may have "just" missed making the cutoff in most popular list. A probabilistic variant of recommendation has been introduced as an alternative to most popular list. Classical results from urn models are used to derive theoretical results for special cases, and to study specific properties of the probabilistic recommender. In addition to simulations, various statistical methodologies are used, such as regression based methodologies as part of a broader decision analysis tool. The third essay views firms as agents in building regression based empirical models to investigate the impact of outsourcing on firms. Using an economy wide panel data of outsourcing expenses of firms, the third essay first investigates the value addition by the IT backgrounds of project owners in managing IT related projects. Then it investigates the impact of peer-pressure on a firm's outsourcing behavior.
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Using social network information in recommender systemsSudan, Nikita Maple 30 September 2011 (has links)
Recommender Systems are used to select online information relevant
to a given user. Traditional (memory based) recommenders explore the user-item rating matrix and make recommendations based on users who have rated similarly or items that have been rated similarly. With the growing popularity of social networks, recommender systems can benefit from combining history of user preferences with information from the social/trust network of users. This thesis explores two techniques of combining user-item rating history with trust network information to make better user-item rating predictions. The first
approach (SCOAL [5]) simultaneously co-clusters and learns separate models
for each co-cluster. The co-clustering is based on the user features as well as
the rating history. This captures the intuition that certain groups of users have similar preferences for certain groups of items. The grouping of certain users is affected by the similarity in the rating behavior and the trust network.
The second graph-based label propagation approach (MAD [27]) works in a transductive setting and propagates ratings of user-item pairs directly on the
user social graph. We evaluate both approaches on two large public data-sets from Epinions.com and Flixster.com.
The thesis is amongst the first to explore the role of distrust in rating prediction. Since distrust is not as transitive as trust i.e. an enemy's enemy need not be an enemy or a friend, distrust can't directly replace trust in trust
propagation approaches. By using a low dimensional representation of the original trust network in SCOAL, we use distrust as it is and don't propagate it. Using SCOAL, we can pin-point the groups of users and the groups of
items that have the same preference model. Both SCOAL and MAD are able to seamlessly integrate side information such as item-subject and item-author
information into the trust based rating prediction model. / text
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