Semantics-Based News Recommendation with SF-IDF+ International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013) June 13, 2013 Marnix Moerland Michel Capelle Frederik Hogenboom Flavius Frasincar Erasmus University Rotterdam PO Box 1738, NL-3000 DR Rotterdam, the Netherlands

Introduction (1) Recommender systems help users to plough through a massive and increasing amount of information Recommender systems: –Content-based –Collaborative filtering –Hybrid Content-based systems are often term-based Common measure: Term Frequency – Inverse Document Frequency (TF-IDF) as proposed by Salton and Buckley [1988] International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Introduction (2) One could take into account semantics: –Semantic Similarity (SS) recommenders: Jiang & Conrath [1997] Leacock & Chodorow [1998] Lin [1998] Resnik [1995] Wu & Palmer [1994] –Concepts instead of terms → Concept Frequency – Inverse Document Frequency (CF-IDF): Reduces noise caused by non-meaningful terms Yields less terms to evaluate Allows for semantic features, e.g., synonyms Relies on a domain ontology Published at WIMS 2011 International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Introduction (3) One could take into account semantics: –Synsets instead of concepts → Synset Frequency – Inverse Document Frequency (SF-IDF): Similar to CF-IDF Does not rely on a domain ontology Published at WIMS 2012 –Research has shown that relationships like synonymy, hyponymy, … provide structure and contribute to an improved level of interpretability –Hence, we coin SF-IDF+, which additionally accounts for synset semantic relationships International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Introduction (4) Implementations in Ceryx (as a plug-in for Hermes [Frasincar et al., 2009], a news processing framework) What is the performance of semantic recommenders? –SF-IDF+ vs. SF-IDF –SF-IDF+ vs. TF-IDF –SF-IDF+ vs. SS International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Framework: User Profile User profile consists of all read news items Implicit preference for specific topics International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Framework: Preprocessing Before recommendations can be made, each news item is parsed: –Tokenizer –Sentence splitter –Lemmatizer –Part-of-Speech International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Framework: Synsets We make use of the WordNet dictionary and WSD Each word has a set of senses and each sense has a set of semantically equivalent synonyms (synsets): –Turkey: turkey, Meleagris gallopavo (animal) Turkey, Republic of Turkey (country) joker, turkey (annoying person) turkey, bomb, dud (failure) –Fly: fly, aviate, pilot (operate airplane) flee, fly, take flight (run away) Synsets are linked using semantic pointers –Hypernym, hyponym, … International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Framework: TF-IDF Term Frequency: the occurrence of a term t i in a document d j, i.e., Inverse Document Frequency: the occurrence of a term t i in a set of documents D, i.e., And hence International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Framework: SF-IDF Synset Frequency: the occurrence of a synset s i in a document d j, i.e., Inverse Document Frequency: the occurrence of a synset s i in a set of documents D, i.e., And hence International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Framework: SF-IDF+ Synset Frequency: the occurrence of a synset s i and its related synsets r i in a document d j, i.e., Inverse Document Frequency: the occurrence of synsets s i and r i in a set of documents D, i.e., Weighting is applied depending on relations, and hence International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Framework: SS (1) TF-IDF and SF-IDF(+) use cosine similarity: –Two vectors: User profile items scores News message items scores –Measures the cosine of the angle between the vectors Semantic Similarity (SS): –Two vectors: User profile synsets News message synsets –Jiang & Conrath [1997], Resnik [1995], and Lin [1998]: information content of synsets –Leacock & Chodorow [1998] and Wu & Palmer [1994]: path length between synsets International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Framework: SS (2) SS score is calculated by computing the pair-wise similarities between synsets in the unread document u and the user profile r : where W is a vector with all combinations of synsets from r and u that have a common Part-of-Speech, and where sim(u,r) is any of the mentioned SS measures. International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Implementation: Hermes Hermes framework is utilized for building a news personalization service for RSS Its implementation is the Hermes News Portal (HNP): –Programmed in Java –Uses OWL / SPARQL / Jena / GATE / WordNet International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Implementation: Ceryx Ceryx is a plug-in for HNP Uses WordNet / Stanford POS Tagger / JAWS lemmatizer / Lesk WSD Main focus is on recommendation support User profiles are constructed Computes TF-IDF, SF-IDF, SF-IDF+, and SS International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Evaluation (1) Experiment: –We let 19 participants evaluate 100 news items –We use 8 different user profiles focusing on various topics –Ceryx computes TF-IDF, SF-IDF, SF-IDF+, and SS for various cut-off values –F1 scores are evaluated International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Evaluation (2) Results: International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013) TF-IDF SF-IDF+ SS

Evaluation (2) Results: International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Conclusions Common recommendation is performed using TF-IDF Semantics could be considered by considering synsets and their relations Semantics-based recommendation outperforms the classic term-based recommendation Future work: –Employ also the similarity of words (e.g., named entities) missing from WordNet (e.g., based on the Google Distance) –Compare SF-IDF, SF-IDF+, and SS with LDA (latent dirichlet allocation) and ESA (explicit semantic analysis) International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)

Questions International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013)