Added dois

Author: x-tabdeveloping

paper.bib

@@ -218,7 +218,8 @@ @InProceedings{content_recommendation
   address="Cham",
   pages="247--263",
   abstract="We propose a plot-based recommendation system, which is based upon an evaluation of similarity between the plot of a video that was watched by a user and a large amount of plots stored in a movie database. Our system is independent from the number of user ratings, thus it is able to propose famous and beloved movies as well as old or unheard movies/programs that are still strongly related to the content of the video the user has watched. The system implements and compares the two Topic Models, Latent Semantic Allocation (LSA) and Latent Dirichlet Allocation (LDA), on a movie database of two hundred thousand plots that has been constructed by integrating different movie databases in a local NoSQL (MongoDB) DBMS. The topic models behaviour has been examined on the basis of standard metrics and user evaluations, performance assessments with 30 users to compare our tool with a commercial system have been conducted.",
-  isbn="978-3-319-27030-2"
+  isbn="978-3-319-27030-2",
+  doi={10.1007/978-3-319-27030-2_16},
 }
 
 @article{unsupervised_classification,
@@ -248,7 +249,8 @@ @InProceedings{information_retrieval
   address="Berlin, Heidelberg",
   pages="29--41",
   abstract="We explore the utility of different types of topic models for retrieval purposes. Based on prior work, we describe several ways that topic models can be integrated into the retrieval process. We evaluate the effectiveness of different types of topic models within those retrieval approaches. We show that: (1) topic models are effective for document smoothing; (2) more rigorous topic models such as Latent Dirichlet Allocation provide gains over cluster-based models; (3) more elaborate topic models that capture topic dependencies provide no additional gains; (4) smoothing documents by using their similar documents is as effective as smoothing them by using topic models; (5) doing query expansion should utilize topics discovered in the top feedback documents instead of coarse-grained topics from the whole corpus; (6) generally, incorporating topics in the feedback documents for building relevance models can benefit the performance more for queries that have more relevant documents.",
-  isbn="978-3-642-00958-7"
+  isbn="978-3-642-00958-7",
+  doi={10.1007/978-3-642-00958-7_6},
 }
 
 @misc{data_mixers,