Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 73 for “"collaborative filtering"”.
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Collaborative filtering with low regret
Collaborative filtering (CF) is a widely used technique in recommendation systems where recommendations are provided in a content-agnostic manner, and there are two main paradigms in neighborhood-based CF: the user-user paradigm and the item-item paradigm. To recommend to a user in the user-user …
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Feature guided automated collaborative filtering
Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1995.
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A dynamic multi-algorithm collaborative-filtering system
… They can take content-based and/or collaborative-filtering techniques into account. Content-based filtering techniques use information, the so-called metadata, that describe the content in more detail. Collaborative-filtering techniques calculate similarities e.g., between users. All …
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Latent variable model estimation via collaborative filtering
Similarity based collaborative filtering for matrix completion is a popular heuristic that has been used widely across industry in the previous decades to build recommendation systems, due to its simplicity and scalability. However, despite its popularity, there has been little theoretical …
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Developing a Mobile Recommendation System using Collaborative Filtering
Αυτή η διπλωματική εργασία υλοποιεί σύστημα συστάσεων πελάτη-διακομιστή που μετατρέπει την είσοδο ενός χρήστη σε μία ταξινομημένη λίστα συστάσεων μερών και εκδηλώσεων. Ο χρήστης χρησιμοποιεί μία εφαρμογή Android για κινητά για να περάσει τις προτιμήσεις του και ο διακομιστής που διαθέτει όλη την …
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Evaluating collaborative filtering content recommenders for mobile phones
… recommender is proposed to address these issues. Collaborative filtering is a technique developed to make predictions on unobserved items based on the preferences of similar users. User-based collaborative filtering has been identified as a simple, yet reasonably accurate scheme. An evaluation is …
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A collaborative filtering prediction algorithm for ClassRank subject recommendations
Undergraduate students at M.I.T. typically utilize three resources when selecting subjects: course specific evaluations, faculty advisors, and peers. While these resources have distinct advantages, they are all limited in scope. The ClassRank web application has been developed to bridge the gap …
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Distributing information for collaborative filtering on Usenet net news
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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Managing information overload on the Web with collaborative filtering
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
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A review-aware multi-modal neural collaborative filtering recommender system
… This thesis looks at developing a neural collaborative filtering (NCF) recommender system model which incorporates data from multi-modalities, textual data and explicit ratings data (and review sentiment). The primary objectives of this study are twofold. Firstly, the aim is to create and …
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Automating data aggregation for collaborative filtering in Ruby on Rails
Collaborative filtering and information filtering are tried and proven methods of utilizing aggregated data about a website's users to provide catered content. Passive filters are one subset of such algorithms that utilize data about a user's interactions with a website in viewing content, …
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Blind regression : understanding collaborative filtering from matrix completion to tensor completion
Neighborhood-based Collaborative filtering (CF) methods have proven to be successful in practice and are widely applied in commercial recommendation systems. Yet theoretical understanding of their performance is lacking. In this work, we introduce a new framework of Blind Regression which assumes …
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Blind regression : nonparametric regression for latent variable models via collaborative filtering
… the analysis naturally leads to a variant of collaborative filtering, shedding insight into the widespread success of collaborative filtering. Assuming each entry is revealed independently with p = max(m-1+[delta], n-1/2+[delta]) for [delta] > 0, we prove that the expected fraction of our …
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Insurance recommendation engine using a combined collaborative filtering and neural network approach
… and tested using a neural network and collaborative filtering approach. The recommendation engine aims to suggest suitable insurance products for new or existing customers, based on their features or selection history. The collaborative filtering approach used matrix factorization on an …
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MotorMouth--a generic engine for large-scale, real-time automated collaborative filtering
Thesis: M.S., Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1995
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Using collaborative filtering based recommendations to promote the social nature of online learning
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Despite the supporting literature on the social nature of learning (Vygotsky, 1978; Bandura, 1977; Wenger, 1998), the online teaching environments at many of our colleges and universities still do not fully embraced the …
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Using K-means Clustering and Similarity Measure to Deal with Missing Rating in Collaborative Filtering Recommendation Systems
The Collaborative Filtering recommendation systems have been developed to address the information overload problem and personalize the content to the users for business and organizations. However, the Collaborative Filtering approach has its limitation of data sparsity and online scalability …
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SNAP Biclustering
… particularly DNA microarray analysis and collaborative filtering. A motivation for this work has been the biclustering technique known as bicACO, which was the first to use ant colony optimization. As bicACO is time intensive, much emphasis was placed on decreasing SNAP's runtime. The …
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An integer programming clustering approach with application to recommendation systems
… area. Early recommendation systems were based on collaborative filtering, which uses the principle that if two people enjoy the same product they are likely to have common favorites. We present an alternative recommendation approach based on finding clusters of similar customers using integer …
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Natural Language Processing and Recommendation Engine for Stack Overflow Data
… solving. We use natural language processing and collaborative filtering algorithms to build a recommendation engine for Stack Overflow tag predictions. Our pipeline consists of document retrieval (TF-IDF and HOTT), text embedding (Sentence BERT), and classification (multi-label and multi-class). …
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