Development of personalized online systems for web search, recommendations, and e-commerce
Bibliographic record
Abstract
Personalized online systems for Web search, news recommendation, and e-commerce are developed. The process of personalization of online systems consists of three main steps: determining a user's needs, classifying products or services, and matching the user's needs with suitable products or services. A multi-feature based method to automatically classify Web pages into categories of topics hierarchically representing the Web pages is proposed. An approach to modeling and quantifying a user's interests and preferences using the user's Web navigational data is presented. The approach is based on the premise that frequently visiting certain types of content or Web sites indicates that the user is interested in related content or retrieving information from those sites. A personalized search system utilizing a Web user's interest, preference and search context models is developed. A Web user's interest and preference models are constructed and updated by analyzing the user's navigational data and automatically classifying Web pages. A user's search context model is used to determine how the user's interest and preference models impact on his or her search behavior. An algorithm to re-rank search results generated by a conventional search engine is designed to provide a personalized Web search service. A hybrid recommender system of personalized recommendation of news on the Web is developed. Based on the similarities between Web pages and users' models of interest and preference, the Web pages are recommended to the users who are likely interested in the related topics. Moreover, the technique of collaborative filtering is employed, which aims to choose the trusted users and incorporate machine intelligence combined with human efforts. Once trusted users are determined, their behavior on the Web is considered as the manual recommendation part of the system. A method of classifying Web customers for planning customized e-marketing is proposed. The proposed e-marketing approach can be divided into four steps: determining a customer's general interest model, ascertaining a customer's local browsing model, classifying Web customers, and designing a personalized marketing and promotion plan for e-commerce based on the customer classification. Various experiments are carried out to demonstrate the effectiveness of the proposed approaches and systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".