Fawaz Ghali Web 2.0 for the Adaptive Web

2 Overview Web 2.0 User Profile in Web2.0 Recommending Systems Content-based Filtering Collaborative Filtering Hybrid Filtering Recommendations to Groups Social Filtering

3 Web 2.0

4 User Profile in Web 2.0 Examples of explicit data collection: Asking a user to rate an item on a sliding scale. Asking a user to rank a collection of items from favourite to least favourite. Presenting two items to a user and asking him/her to choose the best one. Asking a user to create a list of items that he/she likes.

5 User Profile in Web 2.0 Examples of implicit data collection: Observing the items that a user views in an online store. Analyzing item/user viewing times Keeping a record of the items that a user purchases online. Obtaining a list of items that a user has listened to or watched on his/her computer. Analyzing the user's social network and discovering similar likes and dislikes

6 Recommender Systems Specific type of information filtering (IF) technique that attempts to present information items (movies, music, books) that are likely of interest to the user. Comparing the user's profile to some reference characteristics. These characteristics may be from the information item (content-based approach) or the user's social environment (collaborative filtering).

7 Content-based Filtering Items are used as parameters instead of users. Grouping various items together in groups so consumers can compare them all together. Users use and test the item and give it a rating that is relevant to the item and the item class.

8 Content-based Filtering The items are classified based on the rating. The items are used and tested by the same user or group in order to get an accurate rating. More reading: 8/Book%20-%20The%20Adaptive%20Web/Content- basedRecommendationSystems.pdf 8/Book%20-%20The%20Adaptive%20Web/Content- basedRecommendationSystems.pdf

9 Collaborative Filtering Collaborative filtering is the process of filtering information or patterns using techniques involving collaboration among multiple users. The method of making automatic filtering about the interests of a user by collecting taste information from many users (collaborating).

10 How It Works? Collaborative filtering systems usually take two steps: 1. Look for users who share the same rating patterns with the active user (the user whom the prediction is for). 2. Use the ratings from those like-minded users found in step 1 to calculate a prediction for the active user.

11 Active Collaborative Filtering Peer-to-peer approach: peers, co- workers, and people with similar interests rate products, reports, and other material objects, also sharing this information over the web for other people to see.

12 Active Collaborative Filtering Advantages: Actual rating helps to determine the value of the item, find related items on hand. Disadvantage: The opinion may be biased, requires action by the user, user expectations may not be met.

13 Passive Collaborative Filtering Collects information implicitly. A web browser is used to record a users preferences by following and measuring their actions. These implicit filters are then used to determine what else the user will like and recommend potential items of interest.

14 Explicit vs. Implicit filtering Within active and passive filtering there are explicit and implicit methods for determining user preferences. Explicit collection of user preferences requires the evaluator to indicate a value for the content on a rating scale. Implicit collection does not involve the direct input of opinion by the user, but instead it is assumed that their opinion is implied by their actions (reduces the demand on the user, which can mean that much more data is available)

15 Collaborative Filtering Problems The First-Rater Problem: is caused by new items. The system is unable to generate semantic interconnections to these items and therefore are never recommended. The Cold-Start Problem is caused by new users in the system which have not submitted any ratings. Without any information about the user the system is not able to guess the user's preferences and generate recommendations.

16 Hybrid Recommending System A combination of multiple recommending techniques. Example: Collaborative filtering and content- based techniques. More reading: CS411/2008/Book%20- %20The%20Adaptive%20Web/HybridWebRecom menderSystems.pdf

17 Recommendations to Groups Often the users work in groups. Web 2.0 phenomena. 1. acquiring information about the users preferences; 2. generating recommendations; 3. explaining recommendations; 4. helping users to settle on a final result.

18 Recommendations to Groups Acquiring information about the users preferences. If users specify their preferences explicitly, it may be desirable for them to be able to examine each others preference. What benefits and drawbacks can such examination have, and how can it be supported by the system?

19 Recommendations to Groups The system generates recommendations. Some procedure for predicting the suitability of items for a group as a whole must be applied. What conditions might such a procedure be required to fulfil?

20 Recommendations to Groups The system presents recommendations to the members. The suitability of a solution for the individual members becomes an important aspect of a solution. How can relevant information about suitability for individual members be presented effectively?

21 Recommendations to Groups The system helps the members to reach to agreement on which recommendation (if any) to accept. The final decision is not necessarily made by a single person; negotiation may be required. How can the system facilitate the necessary communication among group members? More reading: CS411/2008/Book%20- %20The%20Adaptive%20Web/RecommendationG roups.pdf CS411/2008/Book%20- %20The%20Adaptive%20Web/RecommendationG roups.pdf

22 Social Filtering Systems Bring users together to satisfy explicit information needs, or interpersonal interests. Compute the similarity between users or groups, given their interests or information needs. More info: ok%20- %20The%20Adaptive%20Web/AdaptiveSupportDistributedColla boration.pdf ok%20- %20The%20Adaptive%20Web/AdaptiveSupportDistributedColla boration.pdf

23 Aggregation of Ratings for Individuals For each candidate item ci and each member mj, the system can predict how mj would evaluate (or rate) cj if he or she were familiar with it: 1. For each candidate ci: – For each member mj predict the rating rij of ci – Compute an aggregate rating Ri from the set {rij}. 2. Recommend the set of candidates with the highest predicted ratings Ri.

24 Example: POLYLENS

25 Review Web 2.0 User Profile in Web2.0 Recommending Systems Content-based Filtering Collaborative Filtering Hybrid Filtering Recommendations to Groups Social Filtering

26 Lab session: Thursday 12 Nov Demonstration of Web 2.0 techniques for the Adaptive Web Collaborative authoring; authoring for collaboration; group-based adaptive authoring; social annotation; recommender authoring tool MOT 2.0 Bring your laptop!

27 Questions and Ideas DCS, Room 318