Article Structure
Abstract
We propose methods for estimating the probability that an entity from an entity database is associated with a web search query.
Introduction
Commercial search engines use query associations in a variety of ways, including the recommendation of related queries in Bing, ‘something different’ in Google, and ‘also try’ and related concepts in Yahoo.
Related Work
We introduce a novel application of significant commercial value: entity recommendations for general Web queries.
Association Model
Task Definition: Consider a collection of entities E. Given a search query q, our task is to compute P(e|q), the probability that an entity 6 is relevant to q, for all e E E.
Entity Recommendation
Query recommendations are pervasive in commercial search engines.
Experimental Results
5.1 Datasets
Conclusion
Learning associations between web queries and entities has many possible applications, including query-entity recommendation, personalization by associating entity vectors to users, and direct advertising.
Topics
random sample
- Table 3 lists query-product associations for five randomly sampled products along with their model scores from Pmle Pintp.Page 7, “Experimental Results”
- We created two samples from the TEST dataset: one randomly sampled by taking click weights into account, and the other sampled uniformly at random.Page 7, “Experimental Results”
- Table 3: Example query-product association scores for a random sample of five products.Page 7, “Experimental Results”
- For our performance metrics, we sampled two sets of queries: (a) Query Set Sample: uniform random sample of 100 queries from the unique queries in the one-month log; and (b) Query Bag Sample: weighted random sample , by query frequency, of 100 queries from the query instances in the one-month log.Page 8, “Experimental Results”
See all papers in Proc. ACL 2011 that mention random sample.
See all papers in Proc. ACL that mention random sample.
Back to top.
co-occurrence
- Step 2 — Session Analysis: We build a query-entity frequency co-occurrence matrix, A, consisting of lel rows and nIEl columns, where each row corresponds to a query and each column to an entity.Page 5, “Entity Recommendation”
- 3'Note that this co-occurrence occurs because q’ was annotated with entity 6 in the same session as q occurred.Page 6, “Experimental Results”
- 5.3.1 Experimental Setup We instantiate our recommendation algorithm from Section 4.2 using session co-occurrence frequenciesPage 7, “Experimental Results”
See all papers in Proc. ACL 2011 that mention co-occurrence.
See all papers in Proc. ACL that mention co-occurrence.
Back to top.
data sparsity
- Smoothing techniques are proposed to address the inherent data sparsity in such graphs, including interpolation using a query synonymy model.Page 1, “Abstract”
- Smoothing techniques can be useful to alleviate data sparsity problems common in statistical models.Page 4, “Association Model”
- We expect our smoothing models to have much more impact on M SE (i.e., the tail) than on M SEW since head queries do not suffer from data sparsity .Page 6, “Experimental Results”
See all papers in Proc. ACL 2011 that mention data sparsity.
See all papers in Proc. ACL that mention data sparsity.
Back to top.
knowledge base
- In this paper, we focus instead on associating surface contexts with entities that refer to a particular entry in a knowledge base such as Freebase, IMDB, Amazon’s product catalog, or The Library of Congress.Page 1, “Introduction”
- Clicked results in a vertical search engine are edges between queries and entities e in the vertical’s knowledge base .Page 3, “Association Model”
- Throughout our models, we make the simplifying assumption that the knowledge base E is complete.Page 3, “Association Model”
See all papers in Proc. ACL 2011 that mention knowledge base.
See all papers in Proc. ACL that mention knowledge base.
Back to top.
Model Parameters
- Basic Interpolation: This smoothing model, Pinw(e|q), linearly combines our foreground and background models using a model parameter 04:Page 5, “Association Model”
- Section 5.2 outlines our procedure for leam-ing the model parameters for both 15mm(e|q) andPage 5, “Association Model”
- 5.2.1 Model ParametersPage 6, “Experimental Results”
See all papers in Proc. ACL 2011 that mention Model Parameters.
See all papers in Proc. ACL that mention Model Parameters.
Back to top.
