17,591 research outputs found
Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction
Recommendation plays an increasingly important role in our daily lives.
Recommender systems automatically suggest items to users that might be
interesting for them. Recent studies illustrate that incorporating social trust
in Matrix Factorization methods demonstrably improves accuracy of rating
prediction. Such approaches mainly use the trust scores explicitly expressed by
users. However, it is often challenging to have users provide explicit trust
scores of each other. There exist quite a few works, which propose Trust
Metrics to compute and predict trust scores between users based on their
interactions. In this paper, first we present how social relation can be
extracted from users' ratings to items by describing Hellinger distance between
users in recommender systems. Then, we propose to incorporate the predicted
trust scores into social matrix factorization models. By analyzing social
relation extraction from three well-known real-world datasets, which both:
trust and recommendation data available, we conclude that using the implicit
social relation in social recommendation techniques has almost the same
performance compared to the actual trust scores explicitly expressed by users.
Hence, we build our method, called Hell-TrustSVD, on top of the
state-of-the-art social recommendation technique to incorporate both the
extracted implicit social relations and ratings given by users on the
prediction of items for an active user. To the best of our knowledge, this is
the first work to extend TrustSVD with extracted social trust information. The
experimental results support the idea of employing implicit trust into matrix
factorization whenever explicit trust is not available, can perform much better
than the state-of-the-art approaches in user rating prediction
Poster: Improving Bug Localization with Report Quality Dynamics and Query Reformulation
Recent findings from a user study suggest that IR-based bug localization
techniques do not perform well if the bug report lacks rich structured
information such as relevant program entity names. On the contrary, excessive
structured information such as stack traces in the bug report might always not
be helpful for the automated bug localization. In this paper, we conduct a
large empirical study using 5,500 bug reports from eight subject systems and
replicating three existing studies from the literature. Our findings (1)
empirically demonstrate how quality dynamics of bug reports affect the
performances of IR-based bug localization, and (2) suggest potential ways
(e.g., query reformulations) to overcome such limitations.Comment: The 40th International Conference on Software Engineering (Companion
volume, Poster Track) (ICSE 2018), pp. 348--349, Gothenburg, Sweden, May,
201
Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance
We introduce a new measure of distance between languages based on word
embedding, called word embedding language divergence (WELD). WELD is defined as
divergence between unified similarity distribution of words between languages.
Using such a measure, we perform language comparison for fifty natural
languages and twelve genetic languages. Our natural language dataset is a
collection of sentence-aligned parallel corpora from bible translations for
fifty languages spanning a variety of language families. Although we use
parallel corpora, which guarantees having the same content in all languages,
interestingly in many cases languages within the same family cluster together.
In addition to natural languages, we perform language comparison for the coding
regions in the genomes of 12 different organisms (4 plants, 6 animals, and two
human subjects). Our result confirms a significant high-level difference in the
genetic language model of humans/animals versus plants. The proposed method is
a step toward defining a quantitative measure of similarity between languages,
with applications in languages classification, genre identification, dialect
identification, and evaluation of translations
A Parsing Scheme for Finding the Design Pattern and Reducing the Development Cost of Reusable Object Oriented Software
Because of the importance of object oriented methodologies, the research in
developing new measure for object oriented system development is getting
increased focus. The most of the metrics need to find the interactions between
the objects and modules for developing necessary metric and an influential
software measure that is attracting the software developers, designers and
researchers. In this paper a new interactions are defined for object oriented
system. Using these interactions, a parser is developed to analyze the existing
architecture of the software. Within the design model, it is necessary for
design classes to collaborate with one another. However, collaboration should
be kept to an acceptable minimum i.e. better designing practice will introduce
low coupling. If a design model is highly coupled, the system is difficult to
implement, to test and to maintain overtime. In case of enhancing software, we
need to introduce or remove module and in that case coupling is the most
important factor to be considered because unnecessary coupling may make the
system unstable and may cause reduction in the system's performance. So
coupling is thought to be a desirable goal in software construction, leading to
better values for external software qualities such as maintainability,
reusability and so on. To test this hypothesis, a good measure of class
coupling is needed. In this paper, based on the developed tool called Design
Analyzer we propose a methodology to reuse an existing system with the
objective of enhancing an existing Object oriented system keeping the coupling
as low as possible.Comment: 15 page
Enabling Fine-Grain Restricted Coset Coding Through Word-Level Compression for PCM
Phase change memory (PCM) has recently emerged as a promising technology to
meet the fast growing demand for large capacity memory in computer systems,
replacing DRAM that is impeded by physical limitations. Multi-level cell (MLC)
PCM offers high density with low per-byte fabrication cost. However, despite
many advantages, such as scalability and low leakage, the energy for
programming intermediate states is considerably larger than programing
single-level cell PCM. In this paper, we study encoding techniques to reduce
write energy for MLC PCM when the encoding granularity is lowered below the
typical cache line size. We observe that encoding data blocks at small
granularity to reduce write energy actually increases the write energy because
of the auxiliary encoding bits. We mitigate this adverse effect by 1) designing
suitable codeword mappings that use fewer auxiliary bits and 2) proposing a new
Word-Level Compression (WLC) which compresses more than 91% of the memory lines
and provides enough room to store the auxiliary data using a novel restricted
coset encoding applied at small data block granularities.
Experimental results show that the proposed encoding at 16-bit data
granularity reduces the write energy by 39%, on average, versus the leading
encoding approach for write energy reduction. Furthermore, it improves
endurance by 20% and is more reliable than the leading approach. Hardware
synthesis evaluation shows that the proposed encoding can be implemented
on-chip with only a nominal area overhead.Comment: 12 page
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