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Weida Pan

dblp:185/4293 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Recommender systems · 53% Query processing and optimization · 47%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Recommender systems › point-of-interest recommendation
restaurant recommendation
0.212016
SI2P: A Restaurant Recommendation System Using Preference Queries over Incomplete Information · Proc. VLDB Endow. 2016
Query processing and optimization
preference query
0.112016
SI2P: A Restaurant Recommendation System Using Preference Queries over Incomplete Information · Proc. VLDB Endow. 2016
Query processing and optimization › preference query
skyline query
0.112016
SI2P: A Restaurant Recommendation System Using Preference Queries over Incomplete Information · Proc. VLDB Endow. 2016
Query processing and optimization › top-k query processing
top-k dominating query
0.112016
SI2P: A Restaurant Recommendation System Using Preference Queries over Incomplete Information · Proc. VLDB Endow. 2016

Methods — techniques the papers use, named apart from their topics

top-k dominating query · 0.2skyline query · 0.2
YearPublicationVenuePosition
2016 SI2P: A Restaurant Recommendation System Using Preference Queries over Incomplete Information
abstract
The incomplete data is universal in many real-life applications due to data integration, the limitation of devices, etc. In this demonstration, we present S i 2 p , a restaurant recommendation System with Preference queries on Incomplete Information. S i 2 p is capable of friendly recommending desirable restaurants based on preference queries that take the incomplete ratings information into consideration. It adopts the browser-server model, and incorporates three functionality modules including friendly and convenient query submission , flexible and useful result explanation , timely and incremental dataset interaction. S i 2 p provides the server side based on an extended PostgreSQL database that integrates two types of preference queries, namely, skyline and top- k dominating queries over incomplete data. It also offers the browser-based interface for the users to interact with the system. Using a real restaurant dataset from TripAdvisor, we demonstrate S i 2 p can recommend and explore the restaurants in a friendly way.
Xiaoye Miao, Yunjun Gao, Gang Chen 0001, Huiyong Cui, Weida Pan
Proc. VLDB Endow.6