Research on Application of Lucene Search Engine in Social Network Platform
Bibliographic record
Abstract
With the development of Web2.0 technology, social networks begin to play an increasingly important role in people's life.Widely used social network for researchers has brought some potentially useful information, such as the user's interests and preferences.At the same time, constant search results provided by the search engine can not meet the individual needs of users, a new way search engine, personalized search is an urgent need to explore.Based on this demand, the paper from Sina microblog mining user interests, and the use of open-source Lucene search engine completes personalized search.In this paper, the structure and principle of Lucene search engine are summarized and some related knowledge are introduced, such as text preprocessing and vector space model.Then, this article proposes the Lucene TagMatch Ranking (LTR) algorithm.The main idea is using user's Sina microblog texts to extract tags of interest and measure the matching degree between web pages and user's interests which named value of tag matching degree by the vector space model, then combine the traditional Lucene scoring mechanism, finally realize personalized ranking results based on the user's interest.At last the Eclipse programming algorithm based on Java is used to carry out comparison experiment to confirm the effectiveness of the algorithm.The results are presented to the user in the ranking which based on the user's interests, it will be able to reach recommendation algorithm based on user's interests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".