COLLABORATIVE SEARCH TECHNIQUES

1. COLLABORATIVE SEARCH TECHNIQUES Submitted By: Shikha Singla MIT-872-2K11 M.Tech(2nd Sem) Information Technology

2. Personalized Search Systems • There is a large gap between how well search engine could perform if results were refer to the individual,& how well they currently perform by returning results to satisfy everyone. This gap is called Potential For Personalization.

3. Relevance Judgments • We can explore variation of different people by three data sources:- • Explicit Relevance judgment • Behavior based Implicit Relevance judgment • Content based Implicit Relevance judgment • These variations are used to give different results to different people for the same query.

4. INTRODUCTION • Collaborative search systems are the systems which supports group based searching, Like in knowledge work & education work. • Understanding the similar properties of people involved in group search sessions has the potential to significantly improve collaborative search systems. • To know about the similar properties of different people, we use potential for personalization.

5. Group Formation • We aim to provide analogous support for collaborative Web search by identifying relevant properties of groups of users. We make groups of different users on the basis of two axis:- • Group Longevity • Group Identification

6. Group Longevity • Task based Groups(short term groups) Group members are working together to accomplish this shared task, like- travel planning, shopping etc • Trait based Groups( long term groups) Long-term groups are comprised of users who are related through shared traits, like-geography, occupation(job role or job team groups), demographics etc.

7. Group Identification • Explicit Groups- Group membership can be determined is by information provided directly from the members. Like-gender, age, geographic location, job-role etc. • Implicit Groups- Group membership can be inferred from member activity ,based on their actions. Like- similar desktop indices, grouping users who issue similar search queries etc.

8. Web browsers and search engines, are not designed to support collaboration. • HCI (human-computer interaction) and IR (information retrieval) researchers have begun to design tools for COLLABORATIVE WEB SEARCH. • we propose three techniques that can enhance the value of collaborative search tools using personalization: • Groupization • Smart Splitting • Group Hit-Highlighting

9. Groupization • Group members’ data is used to rank an individual’s search results by giving higher weights to pages that are relevant to more members of the group. • To perform groupization on a set of search results :- • First calculate a personalization score for each search result for each member of the group. • Then for each search result, the groupization score is computed as the sum of the personalization scores of each group member.

10. Then take a weighted combination of the groupization score and the search result’s original rank. • In this, we computed the normalized Discounted Cumulative Gain (DCG)(eg-for any query) From this we can see that, the use of group data in addition to an individual’s led to a greater improvement .

11. Smart Splitting • Collaborative search tools should support division of labor. • In this we do following steps- • One member of the group enters a query term, which is then sent to a search engine. • The top results for this query on the basis of personalized score of every user are then divided up round-robin style amongst all of the group members.

12. Split searching can be used to allow group members to evaluate sets of results efficiently, without redundancy. • In this, we computed the normalized Discounted Cumulative Gain (DCG)(e.g.-for any query) From this we can see that, DCG scores shows smart splitting performing better than the other two methods.

13. Group Hit-Highlighting • Hit-highlighting is a technique used by most major search engines to help users understand how relevant a result is to their information need. • Instances of the user’s keywords that appear within the title, snippet, or url of each search result in the results list are emphasized & all group members’ keywords that appear within a search result are also emphasized. • Then the rank is calculated by comparing the number of times those group queries’ keywords appeared within the result.

14. Conclusion • The design of collaborative search systems can benefit from reflecting on single-user techniques, such as personalization, and considering how they might be applied to groups. like now we are interested in studying explicit task based group. • Instantiating these concepts within a collaborative search tool is an important next step for understanding their impact on group dynamics and collaboration strategies

15. References • Potential For Personalization: By Jaime Teevan, Susan T. Dumais & Eric Horvitz, Microsoft Research. • Understanding Groups’ Properties as a Means of Improving Collaborative Search Systems: By Meredith Ringel Morris & Jaime Teevan ,Microsoft Research, Redmond, WA, USA. • Enhancing Collaborative Web Search with Personalization: Groupization,Smart Splitting,and Group Hit-Highlighting: By Meredith Ringel Morris, Jaime Teevan, &Steve Bush, Microsoft Research, Redmond, WA, USA

16. THANK YOU

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