Using Collaborative Filtering to Model Argument Selection

This study evaluates whether model-based Collaborative Filtering (CF) algorithms, which have been extensively studied and widely used to build recommender systems, can be used to predict which common nouns a predicate can take as its complement. We find that, when trained on verb-noun co-occurrence data drawn from the Corpus of Contemporary American-English (COCA), two popular model-based CF algorithms, Singular Value Decomposition and Non-negative Matrix Factorization, perform well on this task, each achieving an AUROC of at least 0.89 and surpassing several different baselines. We then show that the embedding-vectors for verbs and nouns learned by the two CF models can be quantized (via application of k-means clustering) with minimal loss of performance on the prediction task while only using a small number of verb and noun clusters (relative to the number of distinct verbs and nouns). Finally we evaluate the alignment between the quantized embedding vectors for verbs and the Levin verb classes, finding that the alignment surpassed several randomized baselines. We conclude by discussing how model-based CF algorithms might be applied to learning restrictions on constituent selection between various lexical categories and how these (learned) models could then be used to augment a (rule-based) constituency grammar.

Similarity calculation for recommender systems

Recommender systems represents a class of systems designed to help individuals deal with information overload or incomplete information. Such systems help individuals by providing recommendation through the use of various personalization techniques. Collaborative filtering is a widely used technique for rating prediction in recommender systems. This paper presents a method uses preference relations instead of absolute ratings for similarity calculation. The result indicates that the proposed method outperform the other methods such as the Somers Coefficient.

Approach for the integration of filtering techniques with the search engines

We offer in this approach the integration of search engines with filtering techniques, through the dynamic relationship of hybridization between collaborative filtering and content based filtering in order to solve the past limitations and improve precision and recall of retrieved documents. The approach uses Domain ontology model in the representation of user profile to reduce errors and confusion resulting from consideration for user profile as a single entity, as well as taking advantage from user activity for adaptation of user profile to reflect the state of user.