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Recommendation Systems
Published in Yulei Wu, Fei Hu, Geyong Min, Albert Y. Zomaya, Big Data and Computational Intelligence in Networking, 2017
Normalized discounted cumulative gain (NDCG) is a popular metric for measuring the effectiveness of a web search engine or other information retrieval applications. Discounted Cumulative Gain (DCG) measures the usefulness of an item based on its rank in a recommended list. NDCG normalizes the DCG in order to compensate for the varying length of the recommendation list. For this, the DCG score is divided by the maximum possible gain with the given list. This is conceptually similar to HLU, but gives slightly different result. Formal definition of DCG and NDCG with a list of p recommendations is given by
SAMA: a real-time Web search architecture
Published in International Journal of Computers and Applications, 2022
The information retrieval evaluation campaign provides training and testing sets of queries, whereas the relevance judgment team provides the proposed solution for each query. Experimentally, web information retrieval approaches are evaluated for two tasks: diversity and ad hoc. The diversity task is similar to the ad hoc task, but they differ in the evaluation metrics and judging process. The final goal is to provide a complete coverage and ranked list of pages for a query that aim together to avoid excessive redundancy. The primary effectiveness measure for both tasks is specified by measuring the graded precision of top ten results or graded precision (GP) at k, and documents can be judged as Nav, Key, Hrel, Rel, or Non-relevant. The relevancy of selected resource is determined by calculating GP in the subset of results [30,31]. While a Normalized Discounted Cumulative Gain (nDCG) is the metric of measuring ranking quality by maximizing the relevancy as a whole, it takes into account the graded relevance levels of documents within top ten. Similarly, selecting vertical for a given query is determined by the best running of search query in all resources involved in that vertical. It means the relevancy of vertical is computed by the maximum GP of its resources. In the final analysis, the GP of a vertical is specified by a threshold since some queries have a small set of relevant verticals (we assume 0.5 was a sensitive relevant precision). If no vertical was selected for a given query, the top vertical with maximum relevancy would be selected as relevant.
An empirical evaluation of text representation schemes to filter the social media stream
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
Sandip Modha, Prasenjit Majumder, Thomas Mandl
The Normal Discounted cumulative Gain (nDCG) is used to measure the quality of a ranking list. NDCG allows graded relevance values as opposed to binary values, which are necessary for Precision and Recall. It gives more importance to highly relevant documents than non-relevant or partially relevant documents. A gain is defined, which is accumulated as a user scrolls down the result list (Järvelin & Kekäläinen, 2002). Appearances of relevant documents lower in the list are punished by a discount, which increases with the position. Therefore, nDCG is a more strict metric than Precision, Recall, and F1-score.
Intelligent recommendation model of tourist places based on collaborative filtering and user preferences
Published in Applied Artificial Intelligence, 2023
In terms of evaluation methods, this paper uses the mean absolute error (MAE) and root mean square error (RMSE) to evaluate the algorithm. Normalized discounted cumulative gain (nDCG) is an evaluation metric to measure the accuracy of recommendation lists in information retrieval. In this paper, we use nDCG to test the recommended results. nDCG is a number between (0,1). The larger the value of nDCG, the more accurate the ranking of the items in the recommendation list and the higher the accuracy of the recommendation.