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.
Results
Showing 1 to 14 of 14 for “"Implicit Feedback"”.
-
JoVA-hinge: joint variational autoencoders for personalized recommendation with implicit feedback
… performance in collaborative filtering (CF) with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user preferences. However, existing VAE-based recommendation models learn user and item representations separately. This thesis introduces …
-
A study of language models for exploiting user feedback in Information Retrieval
Feedback is an important technique in Information Retrieval to have users provide contextual information about their search needs, with the goal of improving retrieval accuracy and achieving personalization. Relevance feedback has been studied extensively, and in recent years new types of feedback …
-
Colombus: providing personalized recommendations for drifting user interests
… gathering from a combination of explicit and implicit feedback could allow such systems to detect their search requirements and present additional information, with the least possible effort from them. In this paper, we describe the design, development and evaluation of Colombus, a system …
-
Insurance recommendation engine using a combined collaborative filtering and neural network approach
… produced 0.13 root mean square error based on implicit feedback rating of 0-1, and an overall Top-3 classification accuracy (ability to predict one of the top 3 choices of a customer) of 83.8%. The neural network system achieved an accuracy of 77.2% on Top-3 classification. The system thus …
-
Uncertainty in Recommender Systems
… branches: prediction uncertainty in explicit feedback-based systems, prediction uncertainty in implicit feedback-based systems, and label uncertainty in implicit feedback systems. While this dissertation proposes new uncertainty estimation methods, the novel work in this dissertation is not …
-
Group representation learning for group recommendation
… this problem as group recommendation from group implicit feedback, we focus on two of its practical instances: Given a set of groups and their observed decisions, group decision prediction intends to predict the decision of a new group of users whereas reverse social choice aims to infer the …
-
The relationship between actions and significance of email
… prediction by using action summaries as implicit feedback at scale. Evaluation results suggest that the resulting significance predictions have positive agreement with human assessments, albeit not at statistically strong levels. We speculate that we may require personalized significance …
-
Entity recommendation and search in heterogeneous information networks
… in heterogeneous information network scope with implicit feedback. Second, I study a real-world large-scale entity recommendation application with commercial search engine user logs and a web-scale entity graph. Third, I combine text information and heterogeneous relationships between entities to …
-
Leveraging heterogeneous information networks for personalized entity recommendation
… high-quality personalized recommendations from implicit feedback represented in heterogeneous information networks. We begin by introducing meta-path-based latent features, which capture the connectivity of entities in the network along different paths, giving us a foundation which explicitly …
-
The Use of the CAfFEINE Framework in a Step-by-Step Assembly Guide
… of this thesis is to explore using affect as an implicit feedback channel so that such mistakes would be easily corrected in real time. The CAfFEINE Framework, which was created by Dr. Saha, is a context-aware affective feedback loop in an intelligent environment. For the research described in …
-
Providing personalised information based on individual interests and preferences.
… are one of the most important sources from which implicit feedback is detected through their profiles. These provide valuable information based on which alternative learning approaches (i.e. dwell-based search) can be incorporated into the IR standard measures (i.e. tf-idf) allowing a further …
-
Modified output in response to clarification requests and second language
… of modified output was triggered by one type of implicit feedback, clarification requests. The data were collected from 28 undergraduate students who were learning Japanese as a foreign language. The target linguistic feature was the negation of adjectives in Japanese, and a total of 1,011 …
-
Axiomatic analysis of smoothing methods in language models for pseudo-relevance feedback
Pseudo-Relevance Feedback (PRF) is an important general technique for improving retrieval effectiveness without requiring any user effort. Several state-of-the-art PRF models are based on the language modeling approach where a query language model is learned based on feedback documents. In all …