An Information-Theoretic Approach to Interest Making
The Internet has brought a new meaning to the term communities. Geography is no longer a barrier to international communications. However, the paradigm of meeting new interesting people remains entrenched in traditional means; meeting new interesting people on the Internet still relies on chance and...
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2023
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| Online Access: | https://hdl.handle.net/1721.1/149933 |
| _version_ | 1811096284432433152 |
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| author | Koh, Waikit |
| author2 | Szolovits, Peter |
| author_facet | Szolovits, Peter Koh, Waikit |
| author_sort | Koh, Waikit |
| collection | MIT |
| description | The Internet has brought a new meaning to the term communities. Geography is no longer a barrier to international communications. However, the paradigm of meeting new interesting people remains entrenched in traditional means; meeting new interesting people on the Internet still relies on chance and contacts. This thesis explores a new approach towards matching users in online communities in an effective fashion. Instead of using the conventional feature vector scheme to profile users, each user is represented by a personalized concept hierarchy (or an ontology) that is learnt from the user's behavior in the system. Each concept hierarchy is then interpreted within the Information Theory framework as a probabilistic decision tree. The matching algorithm uses the Kullback-Leiber distance as a measure of deviation between two probabilistic decision trees. Thus, in an online community, where a personalized concept hierarchy represents each user, the Kullback-Leiber distance imposes a full- order rank on the level of similarity of all the users with respect to a particular user in question. The validity and utility of the proposed scheme of matching users is then applied in a set of simulations, using the feature-vector-overlap measure as a baseline. The results of the simulations show that the Kullback Leiber distance, when used in conjunction with the concept hierarchy, is more robust to noise and is able to make a stronger and more distinctive classification of users into similar groups in comparison to the conventional keyword-overlap scheme. A graphical agent system that relies upon the ontology-based interest matching algorithm, called the Collaborative Sanctioning Network, is also described in this thesis. |
| first_indexed | 2024-09-23T16:41:27Z |
| id | mit-1721.1/149933 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:41:27Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | mit-1721.1/1499332023-03-30T03:04:19Z An Information-Theoretic Approach to Interest Making Koh, Waikit Szolovits, Peter The Internet has brought a new meaning to the term communities. Geography is no longer a barrier to international communications. However, the paradigm of meeting new interesting people remains entrenched in traditional means; meeting new interesting people on the Internet still relies on chance and contacts. This thesis explores a new approach towards matching users in online communities in an effective fashion. Instead of using the conventional feature vector scheme to profile users, each user is represented by a personalized concept hierarchy (or an ontology) that is learnt from the user's behavior in the system. Each concept hierarchy is then interpreted within the Information Theory framework as a probabilistic decision tree. The matching algorithm uses the Kullback-Leiber distance as a measure of deviation between two probabilistic decision trees. Thus, in an online community, where a personalized concept hierarchy represents each user, the Kullback-Leiber distance imposes a full- order rank on the level of similarity of all the users with respect to a particular user in question. The validity and utility of the proposed scheme of matching users is then applied in a set of simulations, using the feature-vector-overlap measure as a baseline. The results of the simulations show that the Kullback Leiber distance, when used in conjunction with the concept hierarchy, is more robust to noise and is able to make a stronger and more distinctive classification of users into similar groups in comparison to the conventional keyword-overlap scheme. A graphical agent system that relies upon the ontology-based interest matching algorithm, called the Collaborative Sanctioning Network, is also described in this thesis. 2023-03-29T15:34:59Z 2023-03-29T15:34:59Z 2001-05 https://hdl.handle.net/1721.1/149933 MIT-LCS-TR-830 application/pdf |
| spellingShingle | Koh, Waikit An Information-Theoretic Approach to Interest Making |
| title | An Information-Theoretic Approach to Interest Making |
| title_full | An Information-Theoretic Approach to Interest Making |
| title_fullStr | An Information-Theoretic Approach to Interest Making |
| title_full_unstemmed | An Information-Theoretic Approach to Interest Making |
| title_short | An Information-Theoretic Approach to Interest Making |
| title_sort | information theoretic approach to interest making |
| url | https://hdl.handle.net/1721.1/149933 |
| work_keys_str_mv | AT kohwaikit aninformationtheoreticapproachtointerestmaking AT kohwaikit informationtheoreticapproachtointerestmaking |