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COMP423 Intelligent Agents. Recommender systems Two approaches – Collaborative Filtering Based on feedback from other users who have rated a similar set.

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Presentation on theme: "COMP423 Intelligent Agents. Recommender systems Two approaches – Collaborative Filtering Based on feedback from other users who have rated a similar set."— Presentation transcript:

1 COMP423 Intelligent Agents

2 Recommender systems Two approaches – Collaborative Filtering Based on feedback from other users who have rated a similar set of items in the past – Content based filtering (e.g SmartMuseum) Based on how well the contend of the target item matches the user’s preferred content pattern, which is learnt from the user’s own past ratings and the content pattern of the rated items. – Hybrid

3 User-based Collaborative Filtering Nearest Neighbor Collaborative Filtering – Calculate user similarities Pearson’s correlation – Define the effective neighborhood – Computer the predicted ratings The correlation of two users ken and lee, they both ranked n items K(1..n) L (1..n) Prediction on Ken’s ranking for m

4 Item-based Collaborative filtering Item ranking Matrix Item vectors: the columns Item similarity – Pearson’s Correlation – Cosine similarity – Adjusted Cosine similarity

5 typical Collaborative Filtering Memory based collaborative filtering – Nearest-neighbor based – User similarity – Item similarity Clustering for collaborative filtering – Kmeans – HAC – Naïve Bayes clustering – Group oriented, less personalized, can be addressed by reducing cluster size

6 Content based filtering Content – Features: Movie: directors, actor/actress, producers., editors, distributors, editors, keywords, review, …. Text recommendation: a set of extracted keywords Classification problem

7 Hybrid Collaborative filtering: – Require other users rating data (cold start problem) – Can do cross domain – Non-transitive association problem: users are linked by common items and items are linked by common users. Content Based – Require one user’s rating data – Require item’s content data – Not cross domain Sequential Hybridization Combinational Hybridization

8 Evaluation Binary: change rates to positive or negative – Precision – Top N precision – Recall – F-measure – MAP: consider ranking, precision, recall Mean of the Average Precision for all queries Average Precision: the mean of the precision when each relevant document is retrieved. (M: No of relevant documents) Average precision is roughly the area under the precision and recall curve

9 Evaluation Consider ranking score MAE: mean absolute error

10 Research projects Recommender systems combined with personalized search – Building profile from click through data – Query expansion based on profile Two way recommendation – Online dating systems Knowledge-based, Personalized recommendation

11 Opinion mining Document level Sentence level Feature level

12 Bing Liu, UIC ACL Feature-based Summary (Hu and Liu, KDD-04) GREAT Camera., Jun 3, 2004 Reviewer: jprice174 from Atlanta, Ga. I did a lot of research last year before I bought this camera... It kinda hurt to leave behind my beloved nikon 35mm SLR, but I was going to Italy, and I needed something smaller, and digital. The pictures coming out of this camera are amazing. The 'auto' feature takes great pictures most of the time. And with digital, you're not wasting film if the picture doesn't come out. … …. Feature Based Summary : Feature1: picture Positive: 12 The pictures coming out of this camera are amazing. Overall this is a good camera with a really good picture clarity. … Negative: 2 The pictures come out hazy if your hands shake even for a moment during the entire process of taking a picture. Focusing on a display rack about 20 feet away in a brightly lit room during day time, pictures produced by this camera were blurry and in a shade of orange. Feature2: battery life …

13 Bing Liu, UIC ACL Visual summarization & comparison Summary of reviews of Digital camera 1 PictureBatterySizeWeightZoom + _ Comparison of reviews of Digital camera 1 Digital camera 2 _ +

14 Opinion mining and sentiment analysis Classification Extraction Summarization Supervised, unsupervised Corpus based, dictionary based

15 Opinion mining Opinion holder, object and opinions(P, N) Comparative relations – A is cheaper than B Temporal opinion mining and summarization

16 Projects Web of things Hardware and software Cross domain learning Personalized search learning large Knowledge base Cross checking with Cyc, wordnet Privacy

17 Web data mining Web Content mining Web structure mining Web usage mining

18 Two projects on security Intrusion detection by clustering Web log files – New similarity measure Malicious Web pages Automatic detection


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