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The Study and The Trend of Recommender Systems (RS)

朝陽科技大學資訊管理系 李 麗 華 教 授 2012/12/18. The Study and The Trend of Recommender Systems (RS). Contents. Preface -- Stay Hunger Stay Foolish. Review of Recommendation Systems. Techniques for Recommendation Systems. Applications of Recommendation Systems. The Trend of Recommendation Systems.

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The Study and The Trend of Recommender Systems (RS)

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  1. 朝陽科技大學資訊管理系 李 麗 華 教 授 2012/12/18 The Study and The Trend of Recommender Systems (RS)

  2. Contents Preface -- Stay Hunger Stay Foolish Review of Recommendation Systems Techniques for Recommendation Systems Applications of Recommendation Systems The Trend of Recommendation Systems

  3. Q & A Q: What is recommendation? Q: What is recommendation system? (以下簡稱RS)

  4. An Example

  5. An Example 推薦區

  6. Review of Recommender Systems The Recommendation Systems (RS) History: • Information Retrieval (資料擷取) assumes to have a quite constant underlying database of items and aids the users with changing interests. • Information Filtering (資料過濾) assumes that to access highly dynamic information sources with rather stable users’ interests. • RS are like the dynamic information filtering systems. • RS try to anticipate the users’ needs, and they can be used as decision tools in case of users absences.

  7. Review of Recommender Systems 取得、過濾、預測出 有用且具效益的資訊 IF RS IR

  8. Q & A Q: Why do we need the RS?

  9. Review of Recommender Systems • RS enhances sales of E-commerce • Browsers into buyer(讓瀏覽者變買者) • Recommender systems can help customers find products they wish to purchase. • Cross-sell(交义銷售) • A site might recommend additional products in the checkout process. • Loyalty(建立顧客忠誠度) • Recommender systems improve loyalty by creating a value-added relationship between the site and the customer. EX: 微軟賣湯

  10. Q & A Q: How to implement the RS? Q: What information do we need to implement RS?

  11. Review of Recommender Systems Personalization(個人化) • RS can introduce users to choose the useful information they interested through personalization. User Profile(個人輪廓) • User profile through the questionnaire, the purchasing products, or the web browsing history are usually analyzed in RS to understand the users’ characteristics, habits, and preference. Filtering & match finding(過濾、媒合) • Filtering method and match finding are used for deriving the closest information for the user. EX: 妙員工

  12. Review of Recommender Systems Characteristics of recommender systems (RS) • Be able to access user profiles or user data for analysis. 2.Be able to use the explicit or implicit information. 3.Usually the similarity functions and the distance functions are used for filtering. 4. Be able to adapt the users Interests shifting. 5. Be able to make possible recommendation or proposals. 6.Be able to take the users’ needs into account. 7.Be able to give an explanation or the confidence coefficient.

  13. Standard processes of recommendation Step1:retrieve and filter items(擷取和過濾) Ex: A user is looking for recent fiction books, and the system should provide him a possible list of books. Step2:elaborate a prediction for every item for a certain user(強化預測) Ex: To return a score (or a judgement ) on the fact that the user will like or not like the item. Step3:generate recommendation to the user(推薦) Ex: The proposal of the recommendation to the user is strictly related to the interface chosen for the recommender, and to the interaction between users and system. Review of Recommender Systems

  14. Collect user data & update customer database Feedback according to user’s new information According to user database Retrieve Elaborative a prediction Generate recommendation Evaluate recommendation results for adjustment Review of Recommender Systems Recommendation System

  15. Retrieve Information (資訊擷取的形式) Explicit information (Q: give an example) Implicit information (Q: give an example) User Profiling (使用者資料檔) The amount of user information required by the recommendation function as input. Demographic data Explicit keywords and ratings Implicit interest indicators Context Review of Recommender Systems

  16. The retrieving of the items (資訊擷取的內容) The user may look for peculiar product Items (digital information) suggestions Solution to select candidate items. to retrieve candidate items to give them a proper suggestion (or prediction or decision) Review of Recommender Systems

  17. The elaboration of the prediction The elaboration of the prediction is done by recommender functions. Qualitative approach: system provide suggestions or preferences such as “prefer” and “not prefer.” Quantitative approach: system provide information with score of likeliness to the item. Review of Recommender Systems EX: 老師你猜錯了 EX:電腦徵婚

  18. Techniques for RS The mostly applied RS methods CB Content Based 內容導向式 Mixed Approach 混合式 CF Collaborative Filtering 協同過濾式

  19. Techniques for RS - CB Content Based (CB) method: find and match information or content based on the active user’s information. CB inherits from classic IR and IF. The advantages of CB approaches CB algorithms are tuned for each user. CB algorithms are able to recommend every item that comprises new item, strange item, and unpopular item . CB algorithms are also able to give an explanation of their predictions.

  20. Content representation of items A set of features The type of the feature The value of the feature A weight between the features The items are objects with several attributes that every attribute has completely different meaning. Firstly, every domain has different attributes. Secondly, it is necessary to decide which features are important. Thirdly, the selection of important features are usedto consider with users. Techniques for RS - CB

  21. Features vs. Terms representation The example can discern between the properties “Tom Cruise” as director of a movie and as main male actor of a movie. Techniques for RS - CB

  22. Q & A Q: How to match the information?

  23. Vector Space Model In this model, every item is represented by the vector of its features. The main advantage of the model It doesn’t require any training phase. It is completely available as soon as enough examples are provided. Techniques for RS - CB

  24. Cosine similarity Example user a = user b = Techniques for RS- CB (1) (2) a a b b

  25. Bayesian Classifiers The goal is to derive the probability that how much an item will belong to a certain category. Techniques for RS- CB P(ci):Prior probability P(ci|dj):Posteriori probability dj: item ci:class dj

  26. example 1 A B 0 C Techniques for RS- CB

  27. Naïve Bayesian Classifiers Making an assumption that features are conditionally independent. May have problem on unbalanced classes. Techniques for RS- CB Class B Features Class A

  28. Q & A Q: Problems for CB?

  29. The power of CB is limited by two factors It can not derive the prediction when the user information (or history records) is not available. Characteristics like the quality or the readiness of a document are typical attributes that can be recognized only by human but difficult for computer. Techniques for RS - CB

  30. Collaborative Filtering (CF) Method:using information about a group of users, rather than the only active user. The idea is to find a subset of users that have similar tastes for making prediction. The basic algorithm for CF consists with the following steps Step1: calculate the similarity between the user A and all the other users. Step2: select a set of users that is similar to user A. Step3: use the set of users for referencing and for making a recommendation. Techniques for RS- CF

  31. Techniques for RS- CF

  32. Pearson Correlation Coefficient (PCC) The PCC between a user a and a user u The covariance function Techniques for RS- CF

  33. Pearson Correlation Coefficient (PCC) The standard deviation Significance weight The similarity function Techniques for RS- CF m :the number of co-rated items

  34. Pearson Correlation Coefficient (PCC) Example Techniques for RS- CF

  35. Selection of neighbors Similarity threshold It selects all the users that have a similarity coefficient greater than a prefixed threshold. Best k-neighbors It simply selects the first k users with the best similarity coefficient and uses them for the prediction. Both the approaches presented have the problem that it is necessary to choose a value only on empirical bases. Techniques for RS- CF

  36. The elaboration of the prediction Prediction functions: higher the correlation between two users, higher the probability score that will generate. The deviation from mean Techniques for RS- CF

  37. Q & A Q: Problems for CF?

  38. Problems of Collaborative Filtering Cold Start It is difficult to find a user with a high similarity coefficient. Sparsity It could generate only low similarity coefficients, or none at all. First Rater It is difficult to give a rating to new items, since they are not rated by anyone. Popularity Bias CF approaches tend to recommend always most popular items, and give low scores to strange items. Techniques for RS- CF

  39. Several works have tried to take the advantages of both CB and CF methods. Mixing final results The simplest function is a weighted sum Techniques for RS - Mixed

  40. Collaboration via Content The idea is to consider the content description or every metadata available, and to apply the PCC directly on the features, rather than at level of items. The advantage is that now it is not needed anymore to calculate the similarity coefficient only on items rated by both users. Techniques for RS - Mixed user a user b

  41. Content Boosted Collaborative Filtering (CBCF) Where there is no score for a movie, they fill in the blank with the prediction of the CBalgorithm for reducing the sparsity problem. CBCF is that they apply the PCC for calculating all the similarities and for finding the k-neighborhood of the active user. Techniques for RS - Mixed

  42. Techniques for RS – other terms • Other techniques of RS • Non-Personalized Recommendations • Each customer gets the same recommendations. • Attribute-Based Recommendations • Content-Based Recommendation

  43. Techniques for RS – other terms • Item-to-Item Correlation • A small set of products that thecustomers have expressed interest in. • People-to-People Correlation • Collaborative filtering

  44. Applications of RS

  45. Applications of RS • Well Known business companies who has applied • RS in their website.

  46. Applications of RS • We find the research papers from SDOL database。 • There are 486,556 articles appeared from 1834 to Sep. 2010. (See next page figure). keywords used are: recommendation, recommender, recommender systems, recommender system, recommendation system, recommending, recommendations, Collaborative Filtering, Content-Based, Personalized recommender system", Hybrid recommender systems, collaborative filters • The annual growing rate of the recommender research has surpassed the previous one after 2000. • the majority of the studies focus on the application aspect.

  47. Applications of RS

  48. Applications of RS

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