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Paper: Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric

In this paper, we present our work to support publishers and editors in finding descriptive tags for e-books through tag recommendations. We propose a hybrid tag recommendation system for e-books, which leverages search query terms from Amazon users and e-book metadata, which is assigned by publishers and editors. Our idea is to mimic the vocabulary of users in Amazon, who search for and review e-books, and to combine these search terms with editor tags in a hybrid tag recommendation approach. In total, we evaluate 19 tag recommendation algorithms on the review content of Amazon users, which reflects the readers' vocabulary. Our results show that we can improve the performance of tag recommender systems for e-books both concerning tag recommendation accuracy, diversity as well as a novel semantic similarity metric, which we also propose in this paper.

Introduction
When people shop for books online in e-book stores such as, e.g., the Amazon Kindle store, they enter search terms with the goal to find e-books that meet their preferences. Such e-books have a variety of metadata such as, e.g., title, author or keywords, which can be used to retrieve e-books that are relevant to the query. As a consequence, from the perspective of e-book publishers and editors, annotating e-books with tags that best describe the content and which meet the vocabulary of users (e.g., when searching and reviewing e-books) is an essential task BIBREF0 .
Problem and aim of this work. Annotating e-books with suitable tags is, however, a complex task as users' vocabulary may differ from the one of editors. Such a vocabulary mismatch yet hinders effective organization and retrieval BIBREF1 of e-books. For example, while editors mostly annotate e-books with descriptive tags that reflect the book's content, Amazon users often search for parts of the book title. In the data we use for the present study (see Section SECREF2 ), we find that around 30% of the Amazon search terms contain parts of e-book titles.
In this paper, we present our work to support editors in the e-book annotation process with tag recommendations BIBREF2 , BIBREF3 . Our idea is to exploit user-generated search query terms in Amazon to mimic the vocabulary of users in Amazon, who search for e-books. We combine these search terms with tags assigned by editors in a hybrid tag recommendation approach. Thus, our aim is to show that we can improve the performance of tag recommender systems for e-books both concerning recommendation accuracy as well as semantic similarity and tag recommendation diversity.
Related work. In tag recommender systems, mostly content-based algorithms (e.g., BIBREF4 , BIBREF5 ) are used to recommend tags to annotate resources such as e-books. In our work, we incorporate both content features of e-books (i.e., title and description text) as well as Amazon search terms to account for the vocabulary of e-book readers.
Concerning the evaluation of tag recommendation systems, most studies focus on measuring the accuracy of tag recommendations (e.g., BIBREF2 ). However, the authors of BIBREF6 suggest also to use beyond-accuracy metrics such as diversity to evaluate the quality of tag recommendations. In our work, we measure recommendation diversity in addition to recommendation accuracy and propose a novel metric termed semantic similarity to validate semantic matches of tag recommendations.
Approach and findings. We exploit editor tags and user-generated search terms as input for tag recommendation approaches. Our evaluation comprises of a rich set of 19 different algorithms to recommend tags for e-books, which we group into (i) popularity-based, (ii) similarity-based (i.e., using content information), and (iii) hybrid approaches. We evaluate our approaches in terms of accuracy, semantic similarity and diversity on the review content of Amazon users, which reflects the readers' vocabulary. With semantic similarity, we measure how semantically similar (based on learned Doc2Vec BIBREF7 embeddings) the list of recommended tags is to the list of relevant tags. We use this additional metric to measure not only exact “hits” of our recommendations but also semantic matches.
Our evaluation results show that combining both data sources enhances the quality of tag recommendations for annotating e-books. Furthermore, approaches that solely train on Amazon search terms provide poor performance in terms of accuracy but deliver good results in terms of semantic similarity and recommendation diversity.

Method
In this section, we describe our dataset as well as our tag recommendation approaches we propose to annotate e-books.

Tag Recommendation Approaches
We implement three types of tag recommendation approaches, i.e., (i) popularity-based, (ii) similarity-based (i.e., using content information), and (iii) hybrid approaches. Due to the lack of personalized tags (i.e., we do not know which user has assigned a tag), we do not implement other types of algorithms such as collaborative filtering BIBREF8 . In total, we evaluate 19 different algorithms to recommend tags for annotating e-books.
Popularity-based approaches. We recommend the most frequently used tags in the dataset, which is a common strategy for tag recommendations BIBREF9 . That is, a most popular INLINEFORM0 approach for editor tags and a most popular INLINEFORM1 approach for Amazon search terms. For e-books, for which we also have author (= INLINEFORM2 and INLINEFORM3 ) or BISAC (= INLINEFORM4 and INLINEFORM5 ) information, we use these features to further filter the recommended tags, i.e., to only recommend tags that were used to annotate e-books of a specific author or a specific BISAC.
We combine both data sources (i.e., editor tags and Amazon search terms) using a round-robin combination strategy, which ensures an equal weight for both sources. This gives us three additional popularity-based algorithms (= INLINEFORM0 , INLINEFORM1 and INLINEFORM2 ).
Similarity-based approaches. We exploit the textual content of e-books (i.e., description or title) to recommend relevant tags BIBREF10 . For this, we first employ a content-based filtering approach BIBREF11 based on TF-IDF BIBREF12 to find top- INLINEFORM0 similar e-books. For each of the similar e-books, we then either extract the assigned editor tags (= INLINEFORM2 and INLINEFORM3 ) or the Amazon search terms (= INLINEFORM4 and INLINEFORM5 ). To combine the tags of the top- INLINEFORM6 similar e-books, we use the cross-source algorithm BIBREF13 , which favors tags that were used to annotate more than one similar e-book (i.e., tags that come from multiple recommendation sources). The final tag relevancy is calculated as: DISPLAYFORM0 
where INLINEFORM0 denotes the number of distinct e-books, which yielded the recommendation of tag INLINEFORM1 , to favor tags that come from multiple sources and INLINEFORM2 is the similarity score of the corresponding e-book. We again use a round-robin strategy to combine both data sources (= INLINEFORM3 and INLINEFORM4 ).
Hybrid approaches. We use the previously mentioned cross-source algorithm BIBREF13 to construct four hybrid recommendation approaches. In this case, tags are favored that are recommended by more than one algorithm.
Hence, to create a popularity-based hybrid (= INLINEFORM0 ), we combine the best three performing popularity-based approaches from the ones (i) without any contextual signal, (ii) with the author as context, and (iii) with BISAC as context. In the case of the similarity-based hybrid (= INLINEFORM1 ), we utilize the two best performing similarity-based approaches from the ones (i) which use the title, and (ii) which use the description text. We further define INLINEFORM2 , a hybrid approach that combines the three popularity-based methods of INLINEFORM3 and the two similarity-based approaches of INLINEFORM4 . Finally, we define INLINEFORM5 as a hybrid approach that uses the best performing popularity-based and the best performing similarity-based approach (see Figure FIGREF11 in Section SECREF4 for more details about the particular algorithm combinations).

Experimental Setup
In this section, we describe our evaluation protocol as well as the measures we use to evaluate and compare our tag recommendation approaches.

Evaluation Protocol
For evaluation, we use the third set of e-book annotations, namely Amazon review keywords. As described in Section SECREF1 , these review keywords are extracted from the Amazon review texts and thus, reflect the users' vocabulary. We evaluate our approaches for the 2,896 e-books, for whom we got review keywords. To follow common practice for tag recommendation evaluation BIBREF14 , we predict the assigned review keywords (= our test set) for respective e-books.

Evaluation Metrics
In this work, we measure (i) recommendation accuracy, (ii) semantic similarity, and (iii) recommendation diversity to evaluate the quality of our approaches from different perspectives.
Recommendation accuracy. We use Normalized Discounted Cumulative Gain (nDCG) BIBREF15 to measure the accuracy of the tag recommendation approaches. The nDCG measure is a standard ranking-dependent metric that not only measures how many tags can be correctly predicted but also takes into account their position in the recommendation list with length of INLINEFORM0 . It is based on the Discounted Cummulative Gain, which is given by: DISPLAYFORM0 
where INLINEFORM0 is a function that returns 1 if the recommended tag at position INLINEFORM1 in the recommended list is relevant. We then calculate DCG@ INLINEFORM2 for every evaluated e-book by dividing DCG@ INLINEFORM3 with the ideal DCG value iDCG@ INLINEFORM4 , which is the highest possible DCG value that can be achieved if all the relevant tags would be recommended in the correct order. It is given by the following formula BIBREF15 : DISPLAYFORM0 
Semantic similarity. One precondition of standard recommendation accuracy measures is that to generate a “hit”, the recommended tag needs to be an exact syntactical match to the one from the test set. When tags are recommended from one data source and compared to tags from another source, this can be problematic. For example, if we recommend the tag “victim” but expect the tag “prey”, we would mark this as a mismatch, therefore being a bad recommendation. But if we know that the corresponding e-book is a crime novel, the recommended tag would be (semantically) descriptive to reflect the book's content. Hence, in this paper, we propose to additionally measure the semantic similarity between recommended tags and tags from the test set (i.e., the Amazon review keywords).
Over the last four years, there have been several notable publications in the area of applying deep learning to uncover semantic relationships between textual content (e.g., by learning word embeddings with Word2Vec BIBREF16 , BIBREF17 ). Based on this, we propose an alternative measure of recommendation quality by learning the semantic relationships from both vocabularies and then using it to compare how semantically similar the recommended tags are to the expected review keywords. For this, we first extract the textual content in the form of the description text, title, editor tags and Amazon search terms of e-books from our dataset. We then train a Doc2Vec BIBREF7 model on the content. Then, we use the model to infer the latent representation for both the complete list of recommended tags as well as the list of expected tags from the test set. Finally, we use the cosine similarity measure to calculate how semantically similar these two lists are.
Recommendation diversity. As defined in BIBREF18 , we calculate recommendation diversity as the average dissimilarity of all pairs of tags in the list of recommended tags. Thus, given a distance function INLINEFORM0 that corresponds to the dissimilarity between two tags INLINEFORM1 and INLINEFORM2 in the list of recommended tags, INLINEFORM3 is given as the average dissimilarity of all pairs of tags: DISPLAYFORM0 
 where INLINEFORM0 is the number of evaluated e-books and the dissimilarity function is defined as INLINEFORM1 . In our experiments, we use the previously trained Doc2Vec model to extract the latent representation of a specific tag. The similarity of two tags INLINEFORM2 is then calculated with the Cosine similarity measure using the latent vector representations of respective tags INLINEFORM3 and INLINEFORM4 .

Results
Concerning tag recommendation accuracy, in this section, we report results for different values of INLINEFORM0 (i.e., number of recommended tags). For the beyond-accuracy experiment, we use the full list of recommended tags (i.e., INLINEFORM1 ).

Recommendation Accuracy Evaluation
Figure FIGREF11 shows the results of the accuracy experiment for the (i) popularity-based, (ii) similarity-based, and (iii) hybrid tag recommendation approaches.
Popularity-based approaches. In Figure FIGREF11 , we see that popularity-based approaches based on editor tags tend to perform better than if trained on Amazon search terms. If we take into account contextual information like BISAC or author, we can further improve accuracy in terms of INLINEFORM0 . That is, we find that using popular tags from e-books of a specific author leads to the best accuracy of the popularity-based approaches. This suggests that editors and readers do seem to reuse tags for e-books of same authors. If we use both editor tags and Amazon search terms, we can further increase accuracy, especially for higher values of INLINEFORM1 like in the case of INLINEFORM2 . This is, however, not the case for INLINEFORM3 as the accuracy of the integrated INLINEFORM4 approach is low. The reason for this is the limited amount of e-books from within the Amazon search query logs that have BISAC information (i.e., only INLINEFORM5 ).
Similarity-based approaches. We further improve accuracy if we first find similar e-books and then extract their top- INLINEFORM0 tags in a cross-source manner as described in Section SECREF4 .
As shown in Figure FIGREF11 , using the description text to find similar e-books results in more accurate tag recommendations than using the title (i.e., INLINEFORM0 for INLINEFORM1 ). This is somehow expected as the description text consists of a bigger corpus of words (i.e., multiple sentences) than the title. Concerning the collected Amazon search query logs, extracting and then recommending tags from this source results in a much lower accuracy performance. Thus, these results also suggest to investigate beyond-accuracy metrics as done in Section SECREF17 .
Hybrid approaches. Figure FIGREF11 shows the accuracy results of the four hybrid approaches. By combining the best three popularity-based approaches, we outperform all of the initially evaluated popularity algorithms (i.e., INLINEFORM0 for INLINEFORM1 ). On the contrary, the combination of the two best performing similarity-based approaches INLINEFORM2 and INLINEFORM3 does not yield better accuracy. The negative impact of using a lower-performing approach such as INLINEFORM4 within a hybrid combination can also be observed in INLINEFORM5 for lower values of INLINEFORM6 . Overall, this confirms our initial intuition that combining the best performing popularity-based approach with the best similarity-based approach should result in the highest accuracy (i.e., INLINEFORM7 for INLINEFORM8 ). Moreover, our goal, namely to exploit editor tags in combination with search terms used by readers to increase the metadata quality of e-books, is shown to be best supported by applying hybrid approaches as they provide the best prediction results.

Figure 2: Beyond-accuracy evaluation results of our tag recommendation approaches. We use the list of 10 recommended tags to calculate the (a) semantic similarity, and (b) recommendation diversity. We provide the boxplots and the mean values for the approaches.

Question: how is diversity measured?
{"answer": ["average dissimilarity of all pairs of tags in the list of recommended tags"], "evidence": ["Recommendation diversity. As defined in BIBREF18 , we calculate recommendation diversity as the average dissimilarity of all pairs of tags in the list of recommended tags. Thus, given a distance function INLINEFORM0 that corresponds to the dissimilarity between two tags INLINEFORM1 and INLINEFORM2 in the list of recommended tags, INLINEFORM3 is given as the average dissimilarity of all pairs of tags: DISPLAYFORM0"]}