Semantics-Based News Recommendation

International Conference on Web Intelligence, Mining, and Semantics (WIMS 2012)

June 14, 2012

Michel Capellemichelcapelle@gmail.com

Marnix Moerlandmarnix.moerland@gmail.com

Flavius Frasincarfrasincar@ese.eur.nl

Frederik Hogenboomfhogenboom@ese.eur.nl

Erasmus University RotterdamPO Box 1738, NL-3000 DRRotterdam, 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 2012)

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 2012)

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

• Implementations in Ceryx (as a plug-in for Hermes [Frasincar et al., 2009], a news processing framework)

• What is the performance of semantic recommenders?– TF-IDF vs. SF-IDF– TF-IDF vs. SS

International Conference on Web Intelligence, Mining, and Semantics (WIMS 2012)

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 2012)

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 2012)

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 2012)

Framework: TF-IDF• Term Frequency: the occurrence of a term ti in a

document dj, i.e.,

• Inverse Document Frequency: the occurrence of a term ti in a set of documents D, i.e.,

• And hence

International Conference on Web Intelligence, Mining, and Semantics (WIMS 2012)

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Framework: SF-IDF• Synset Frequency: the occurrence of a synset si in a

document dj, i.e.,

• Inverse Document Frequency: the occurrence of a synset si in a set of documents D, i.e.,

• And hence

International Conference on Web Intelligence, Mining, and Semantics (WIMS 2012)

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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 2012)

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 2012)

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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 2012)

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, and SS

International Conference on Web Intelligence, Mining, and Semantics (WIMS 2012)

Evaluation (1)• Experiment:

– We let 19 participants evaluate 100 news items– User profile: all articles that are related to Microsoft, its

products, and its competitors– Ceryx computes TF-IDF, SF-IDF, and SS with cut-off of 0.5– Measurements:

• Accuracy• Precision• Recall• Specificity• F1-measure• t-tests for determining significance

International Conference on Web Intelligence, Mining, and Semantics (WIMS 2012)

Evaluation (2)• Results:

– SF-IDF significantly outperforms TF-IDF– Almost all SS methods significantly outperform TF-IDF

International Conference on Web Intelligence, Mining, and Semantics (WIMS 2012)

Measure TF-IDF SF-IDF J&C L&C L R W&P

Accuracy 78.2% 80.1% 78.3% 59.5% 38.1% 74.5% 58.5%

Precision 77.4% 77.8% 64.2% 33.7% 19.9% 56.4% 35.3%

Recall 22.0% 35.9% 29.3% 63.5% 49.7% 40.0% 73.6%

Specificity 97.2% 94.7% 94.6% 57.9% 34.0% 86.3% 52.6%

F1-measure 32.0% 46.8% 38.4% 43.2% 27.7% 42.8% 47.1%

Conclusions• Common recommendation is performed using TF-IDF

• Semantics could be considered by considering synsets:– SF-IDF– SS

• 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 CF-IDF, SF-IDF, and SS with LDA (latent dirichlet

allocation) and ESA (explicit semantic analysis)

International Conference on Web Intelligence, Mining, and Semantics (WIMS 2012)

Questions

International Conference on Web Intelligence, Mining, and Semantics (WIMS 2012)