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- Ho Chung Wu, Robert Wing Pong Luk, Kam-Fai Wong, Kui-Lam Kwok
- ACM Trans. Inf. Syst.
- 2008

A novel probabilistic retrieval model is presented. It forms a basis to interpret the TF-IDF term weights as making relevance decisions. It simulates the local relevance decision-making for every location of a document, and combines all of these “local” relevance decisions as the “document-wide” relevance decision for the document.… (More)

- Ho Chung Wu, Robert Wing Pong Luk, Kam-Fai Wong
- SIGIR
- 2007

This paper presents a new perspective of the probability ranking principle (PRP) by defining retrieval effectiveness in terms of our novel expected rank measure of a set of documents for a particular query. This perspective is based on preserving decision preferences, and it imposes weaker conditions on PRP than the utility-theoretic perspective of PRP.

- Ho Chung Wu, Robert Wing Pong Luk, Kam-Fai Wong, Kui-Lam Kwok, W. J. Li
- SIGIR
- 2005

We propose a novel probabilistic retrieval model which weights terms according to their contexts in documents. The term weighting function of our model is similar to the language model and the binary independence model. The retrospective experiments (i.e., relevance information is present) illustrate the potential of our probabilistic context-based… (More)

- Ho Chung Wu, Robert Wing Pong Luk, Kam-Fai Wong, Kui-Lam Kwok
- CIKM
- 2006

This paper presents our novel relevance feedback (RF) algorithm that uses the probabilistic document-context based retrieval model with limited relevance judgments for document re-ranking. Probabilities of the document-context based retrieval model are estimated from the top <i>N</i> (=20) documents in the initial retrieval. We use document-context based… (More)

- Ho Chung Wu, Robert Wing Pong Luk, Kam-Fai Wong, Kui-Lam Kwok
- Inf. Process. Manage.
- 2007

This paper describes our novel retrieval model that is based on contexts of query terms in documents (i.e., document contexts). Our model is novel because it explicitly takes into account of the document contexts instead of implicitly using the document contexts to find query expansion terms. Our model is based on simulating a user making relevance… (More)

- Edward K. F. Dang, Ho Chung Wu, Robert Wing Pong Luk, Kam-Fai Wong
- ACM Trans. Inf. Syst.
- 2009

A new principles framework is presented for retrieval evaluation of ranked outputs. It applies decision theory to model relevance decision preferences and shows that the Probability Ranking Principle (PRP) specifies optimal ranking. It has two new components, namely a probabilistic evaluation model and a general measure of retrieval effectiveness. Its… (More)

- Ho Chung Wu, Edward K. F. Dang, +5 authors Kam-Fai Wong
- TREC
- 2008

This paper presents the results of our participation in the relevance feedback track using our novel retrieval models. These models simulate human relevance decision-making. For each document location of a query term, information from its document-context at that location determines the relevance decision outcomes there. The relevance values for all… (More)

In the TREC 2005 robust retrieval track, we tested our adaptive retrieval model that automatically switches between the 2-Poisson model/adaptive vector space model and our initial predictive probabilistic context-based model depending on some query characteristics. Our 2-Poisson model uses the BM11 term weighting scheme with passage retrieval and… (More)

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