Query: RainForest—A Framework for Fast Decision Tree Construction of Large Datasets

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SLIQ: A Fast Scalable Classifier for Data Mining <=SEP=> Classi cation is an important problem in the emerging eld of data mining. Although classi cation has been studied extensively in the past, most of the classi cation algorithms are designed only for memory-resident data, thus limiting their suitability for data mining large data sets. This paper discusses issues in building a scalable classier and presents the design of SLIQ, a new classi er. SLIQ is a decision tree classi er that can handle both numeric and categorical attributes. It uses a novel pre-sorting technique in the tree-growth phase. This sorting procedure is integrated with a breadthrst tree growing strategy to enable classi cation of disk-resident datasets. SLIQ also uses a new tree-pruning algorithm that is inexpensive, and results in compact and accurate trees. The combination of these techniques enables SLIQ to scale for large data sets and classify data sets irrespective of the number of classes, attributes, and examples (records), thus making it an attractive tool for data mining.

Sampling-Based Estimation of the Number of Distinct Values of an Attribute <=SEP=> We provide several new sampling-based estimators of the number of distinct values of an attribute in a relation. We compare these new estimators to estimators from the database and statistical literature empirically, using a large number of attribute-value distributions drawn from a variety of real-world databases. This appears to be the first extensive comparison of distinct-value estimators in either the database or statistical literature, and is certainly the first to use highlyskewed data of the sort frequently encountered in database applications. Our experiments indicate that a new “hybrid” estimator yields the highest precision on average for a given sampling fraction. This estimator explicitly takes into account the degree of skew in the data and combines a new “smoothed jackknife” estimator with an estimator due to Shlosser. We investigate how the hybrid estimator behaves as we scale up the size of the database.

SPRINT: A Scalable Parallel Classifier for Data Mining <=SEP=> Classification is an important data mining problem. Although classification is a wellstudied problem, most of the current classification algorithms require that all or a portion of the the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. We present a new decision-tree-based classification algorithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily parallelized, allowing many processors to work together to build a single consistent model. This parallelization, also presented here, exhibits excellent scalability as well. The combination of these characteristics makes the proposed algorithm an ideal tool for data mining.

Programs for Machine Learning. Part I <=SEP=> 

Inferring Decision Trees Using the Minimum Description Length Principle <=SEP=> This paper concerns methods for inferring decision trees from examples for classification problems. The reader who is unfamiliar with this problem may wish to consult J. R. Quinlan’s paper (1986), or the excellent monograph by Breiman et al. (1984), although this paper will be self-contained. This work is inspired by Rissanen’s work on the Minimum description length principle (or MDLP for short) and on his related notion of the stochastic complexity of a string Rissanen, 1986b. The reader may also want to refer to related work by Boulton and Wallace (1968, 1973a, 1973b), Georgeff and Wallace (1984), and Hart (1987). Roughly speaking, the minimum description length principle states that the best “theory” to infer from a set of data is the one which minimizes the sum of

A low dropout, CMOS regulator with high PSR over wideband frequencies <=SEP=> Modern system-on-chip (SoC) environments are swamped in high frequency noise that is generated by RF and digital circuits and propagated onto supply rails through capacitive coupling. In these systems, linear regulators are used to shield noise-sensitive analog blocks from high frequency fluctuations in the power supply. The paper presents a low dropout regulator that achieves power supply rejection (PSR) better than -40 dB over the entire frequency spectrum. The system has an output voltage of 1.0 V and a maximum current capability of 10 mA. It consists of operational amplifiers (op amps), a bandgap reference, a clock generator, and a charge pump and has been designed and simulated using BSIM3 models of a 0.5 /spl mu/m CMOS process obtained from MOSIS.

Optical Fiber Sensors for Aircraft Structural Health Monitoring <=SEP=> Aircraft structures require periodic and scheduled inspection and maintenance operations due to their special operating conditions and the principles of design employed to develop them. Therefore, structural health monitoring has a great potential to reduce the costs related to these operations. Optical fiber sensors applied to the monitoring of aircraft structures provide some advantages over traditional sensors. Several practical applications for structures and engines we have been working on are reported in this article. Fiber Bragg gratings have been analyzed in detail, because they have proved to constitute the most promising technology in this field, and two different alternatives for strain measurements are also described. With regard to engine condition evaluation, we present some results obtained with a reflected intensity-modulated optical fiber sensor for tip clearance and tip timing measurements in a turbine assembled in a wind tunnel.

3DMatch: Learning the Matching of Local 3D Geometry in Range Scans <=SEP=> Establishing correspondences between 3D geometries is essential to a large variety of graphics and vision applications, including 3D reconstruction, localization, and shape matching. Despite significant progress, geometric matching on real-world 3D data is still a challenging task due to the noisy, low-resolution, and incomplete nature of scanning data. These difficulties limit the performance of current state-of-art methods which are typically based on histograms over geometric properties. In this paper, we introduce 3DMatch1, a data-driven local feature learner that jointly learns a geometric feature representation and an associated metric function from a large collection of realworld scanning data. We represent 3D geometry using accumulated distance fields around key-point locations. This representation is suited to handle noisy and partial scanning data, and concurrently supports deep learning with convolutional neural networks directly in 3D. To train the networks, we propose a way to automatically generate correspondence labels for deep learning by leveraging existing RGB-D reconstruction algorithms. In our results, we demonstrate that we are able to outperform state-of-theart approaches by a significant margin. In addition, we show the robustness of our descriptor in a purely geometric sparse bundle adjustment pipeline for 3D reconstruction. 1All of our code, datasets, and trained models are publicly available: http://3dmatch.cs.princeton.edu/

A Hybrid Approach to Grapheme to Phoneme Conversion in Assamese <=SEP=> Assamese is one of the low resource Indian languages. This paper implements both rule-based and data-driven grapheme to phoneme (G2P) conversion systems for Assamese. The rule-based system is used as the baseline which yields a word error rate of 35.3%. The data-driven systems are implemented using state-of-the-art sequence learning techniques such as —i) Joint-Sequence Model (JSM), ii) Recurrent Neural Networks with LTSM cell (LSTM-RNN) and iii) bidirectional LSTM (BiLSTM). The BiLSTM yields the lowest WER i.e., 18.7%, which is an absolute 16.6% improvement on the baseline system. We additionally implement the rules of syllabification for Assamese. The surface output is generated in two forms namely i) phonemic sequence with syllable boundaries, and ii) only phonemic sequence. The output of BiLSTM is fed as an input to Hybrid system. The Hybrid system syllabifies the input phonemic sequences to apply the vowel harmony rules. It also applies the rules of schwa-deletion as well as some rules in which the consonants change their form in clusters. The accuracy of the Hybrid system is 17.3% which is an absolute 1.4% improvement over the BiLSTM based G2P.

An innovative cost-effective smart meter with embedded non intrusive load monitoring <=SEP=> Most of the time, domestic energy usage is invisible to the users. Households have only a vague idea about the amount of energy they are using for different purposes during the day, and the electric bill is usually the only monthly/bimonthly feedback. Making the energy consumption promptly visible can make the difference and foster a really efficient usage of the energy in a city. The emerging smart metering infrastructure has the potential to address some of these goals, by providing both households and electric utilities useful information about energy consumption, with a detailed breakdown to single appliance contribution. This paper presents a novel ultra-low-cost smart metering system capable of providing domestic AC load identification, by implementing both basic/typical smart meter measurement and a simple yet effective load disaggregation algorithm, which can detect and monitor domestic loads starting from a single point of measurement.

A Stable and Accurate Marker-Less Augmented Reality Registration Method <=SEP=> Markerless Augmented Reality (AR) registration using the standard Homography matrix is unstable, and for image-based registration it has very low accuracy. In this paper, we present a new method to improve the stability and the accuracy of marker-less registration in AR. Based on the Visual Simultaneous Localization and Mapping (V-SLAM) framework, our method adds a three-dimensional dense cloud processing step to the state-of-the-art ORB-SLAM in order to deal with mainly the point cloud fusion and the object recognition. Our algorithm for the object recognition process acts as a stabilizer to improve the registration accuracy during the model to the scene transformation process. This has been achieved by integrating the Hough voting algorithm with the Iterative Closest Points(ICP) method. Our proposed AR framework also further increases the registration accuracy with the use of integrated camera poses on the registration of virtual objects. Our experiments show that the proposed method not only accelerates the speed of camera tracking with a standard SLAM system, but also effectively identifies objects and improves the stability of markerless augmented reality applications.

A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues <=SEP=> The best-matching segment is then assigned to this groundtruth bounding box for the evaluation metric described in our paper, as well as for the metric described below. Some previous works have evaluated 3D segmentation using the intersection-over-union metric on 3D points [5]. Note that our method segments the entire scene, as opposed to the method of Wang et al. [5], so the evaluation metric from Wang et al. [5] does not directly apply. However, we could modify the intersection-over-union metric [5] as follows: we can compute the fraction of ground-truth bounding boxes which have an intersection-over-union score less than a threshold τIOU , as

Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting <=SEP=> In this paper, we propose a novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH), to deal with multi-structure data even in the presence of severe outliers. The proposed method formulates geometric model fitting as a mode seeking problem on a hypergraph in which vertices represent model hypotheses and hyperedges denote data points. MSH intuitively detects model instances by a simple and effective mode seeking algorithm. In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a "weight-aware sampling" technique. The proposed method not only alleviates sensitivity to the data distribution, but also is scalable to large scale problems. Experimental results further demonstrate that the proposed method has significant superiority over the state-of-the-art fitting methods on both synthetic data and real images.

Name Translation in Statistical Machine Translation - Learning When to Transliterate <=SEP=> We present a method to transliterate names in the framework of end-to-end statistical machine translation. The system is trained to learn when to transliterate. For Arabic to English MT, we developed and trained a transliterator on a bitext of 7 million sentences and Google’s English terabyte ngrams and achieved better name translation accuracy than 3 out of 4 professional translators. The paper also includes a discussion of challenges in name translation evaluation.

Suppressing the azimuth ambiguities in synthetic aperture radar images <=SEP=> This paper proposes a method for removing the azimuth ambiguities from synthetic aperture radar ( S A R ) images. The basic idea is to generate a two-dimensional reference function for S A R processing which provides, in addition to the matched filtering for the unaliased part of the received signal, the deconvolution of the azimuth ambiguities. This approach corresponds to an ideal filter concept, where an ideal impulse response function is obtained even in the presence of severe phase and amplitude errors. Modeling the sampled azimuth signal shows that the absolute phase value of the ambiguities cannot easily be determined due to their undersampling. The concept of the ideal filter is then extended to accommodate the undefined phase of the ambiguities and also the fading of the azimuth signal. Raw data from the E-SAR system have been used to verify the improvement in image quality by using the new method. It has a substantial significance in enabling the pulse-repetition frequency (PRF) constraints in the S A R system design to be relaxed and also for improving S A R image quality and interpretation.

Swara Histogram Based Structural Analysis And Identification Of Indian Classical Ragas <=SEP=> This work is an attempt towards robust automated analysis of Indian classical ragas through machine learning and signal processing tools and techniques. Indian classical music has a definite heirarchical structure where macro level concepts like thaats and raga are defined in terms of micro entities like swaras and shrutis. Swaras or notes in Indian music are defined only in terms of their relation to one another (akin to the movable do-re-mi-fa system), and an inference must be made from patterns of sounds, rather than their absolute frequency structure. We have developed methods to perform scale-independent raga identification using a random forest classifier on swara histograms and achieved state-of-the-art results for the same. The approach is robust as it directly works on partly noisy raga recordings from Youtube videos without knowledge of the scale used, whereas previous work in this direction often use audios generated in a controlled environment with the desired scale. The current work demonstrates the approach for 8 ragas namely Darbari, Khamaj, Malhar, Sohini, Bahar, Basant, Bhairavi and Yaman and we have achieved an average identification accuracy of 94.28% through the framework.

Monitoring of Micro-sleep and Sleepiness for the Drivers Using EEG Signal <=SEP=> ................................................................................................................................................... 3 ACKNOWLEDGEMENT ................................................................................................................................. 5 LIST OF FIGURES .......................................................................................................................................... 8 LIST OF TABLES ............................................................................................................................................ 9 LIST OF ABBREVIATIONS ............................................................................................................................ 10 INTRODUCTION ......................................................................................................................................... 11 BACKGROUND ........................................................................................................................................... 13 METHODS .................................................................................................................................................. 17 PRE-PROCESSING ................................................................................................................................... 20 FEATURE EXTRACTION ........................................................................................................................... 24 SUPPORT VECTOR MACHINE ................................................................................................................. 31 RELATED WORK ......................................................................................................................................... 41 Extraction of Feature Information in EEG Signal ................................................................................... 41 Sleepiness and health problems in drivers who work driving some kind of vehicle with nocturnal and shift works ............................................................................................................................................. 43 SVM Regression Training with SMO ...................................................................................................... 48 EVALUATION AND CONCLUSION ............................................................................................................... 52 FUTURE WORK ........................................................................................................................................... 55 REFERENCES .............................................................................................................................................. 58

Improving Liver Lesion Detection using CT-to-PET FCN and GAN Networks <=SEP=> We present a brief description of our recent work that was submitted for journal publication introducing a novel system for generation of virtual PET images using CT scans [1]. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to synthesize PET images from given input CT images. Clinically, such solutions may reduce the need for the more expensive and radioactive PET/CT scan. Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver. Current results look promising showing a reduction in the average false positive per case while keeping the same true positive rate. The suggested solution is comprehensive and can be expanded to additional body organs, and different modalities.

Challenges and Solutions for End-to-End Time-Sensitive Optical Networking <=SEP=> Time sensitive applications rely on deterministic low latency, highly reactive, and trustable networks, as opposed to today's best effort internet. We review existing solutions and propose in this paper Deterministic Dynamic Network as a solution for end-to-end time-sensitive optical networking.

Childhood Adversity and Factors Determining Resilience among Undergraduate Students <=SEP=> Childhood adversity experiences, when not properly managed may result to anti-social behaviour, health risk or psychological problems like severe depression, suicidal behavior in undergraduate students. Hence the study examined childhood adversity experiences and its factors determining resilience among undergraduate students in Oyo State. The study adopted a descriptive survey research; multi-stage random sampling technique was used to select 341 undergraduate students of Ladoke Akintola University of Technology, Ogbomosho and Oyo State. Two research instruments were used to test childhood adversity scale and factors determining resilience (protective family, birth order, community protective factors). The study formulated four research hypotheses which were tested with Pearson Product moment correlation analysis and multiple regression analysis at coefficient level of 0.05. The result of the analysis revealed that level of Adverse Childhood Experience (ACE) among respondents is slightly low with half of the respondents between the ages of 18 years to 20 years. The prominent ACE identified by respondents is physical assaults, home with incidence of substance abuse, victim of sexual abuse, and humiliation from parents. The level of resilience among the respondents is moderate. There was a positive significant relationship between childhood adversity and lifetime resilience undergraduate student in Oyo State (r = 0.272), protective family factor was also associated with lifetime resilience undergraduate student in Oyo State (r = 0.018) there was a significant relationship between birth order and lifetime resilience undergraduate student in Oyo State. (r = 0.794) there was a significant relationship between community protective factor and lifetime resilience undergraduate student o in Oyo State (r = 0.835). The magnitude of relative contribution showed protective family factor (β = 0.987), had significant relative contribution; community protective factor (β = 0.762), had significant relative contribution; childhood adversity (β = 0.724), had significant relative contribution; and then birth order (β = 0.687), had significant relative contribution to resilience of the undergraduate students. Educational institutions should tackle issues on educator-student relationships through various channels, especially social work, counseling units and students affairs department. There is need for initial psychosocial assessment for fresher to test resilience and adverse childhood experiences, mental health service should be contextualize in health care service for undergraduate students, the parents should be educated on the impact of childhood adversity on wellbeing and academic performance of undergraduate students in Nigeria.

A 79 GHz differentially fed grid array antenna <=SEP=> This paper presents a planar grid array antenna with a 100 Ω differential microstrip line feed on a single layer of standard soft substrate. The antenna operates in the 79 GHz frequency band for automotive radar applications. Its single row design offers a narrow beam in elevation and a wide beam in azimuth. Together with the differential microstrip line feeding, the antenna is suitable for differential multichannel MMICs in the frequency range.

The Santa Claus problem <=SEP=> We consider the following problem: The Santa Claus has n presents that he wants to distribute among m kids. Each kid has an arbitrary value for each present. Let p<sub>ij</sub> be the value that kid i has for present j. The Santa's goal is to distribute presents in such a way that the least lucky kid is as happy as possible, i.e he tries to maximize min<sub>i=1,...,m</sub> sum<sub>j ∈ S<sub>i</sub></sub> p<sub>ij</sub> where S<sub>i</sub> is a set of presents received by the i-th kid.Our main result is an O(log log m/log log log m) approximation algorithm for the restricted assignment case of the problem when p<sub>ij</sub> ∈ p<sub>j</sub>,0 (i.e. when present j has either value p<sub>j</sub> or 0 for each kid). Our algorithm is based on rounding a certain natural exponentially large linear programming relaxation usually referred to as the configuration LP. We also show that the configuration LP has an integrality gap of Ω(m<sup>1/2</sup>) in the general case, when p<sub>ij</sub> can be arbitrary.

The Wisconsin Card Sorting Test: theoretical analysis and modeling in a neuronal network. <=SEP=> Neuropsychologists commonly use the Wisconsin Card Sorting Test as a test of the integrity of frontal lobe functions. However, an account of its range of validity and of the neuronal mechanisms involved is lacking. We analyze the test at 3 different levels. First, the different versions of the test are described, and the results obtained with normal subjects and brain-lesioned patients are reviewed. Second, a computational analysis is used to reveal what algorithms may pass the test, and to predict their respective performances. At this stage, 3 cognitive components are isolated that may critically contribute to performance: the ability to change the current rule when negative reward occurs, the capacity to memorize previously tested rules in order to avoid testing them twice, and the possibility of rejecting some rules a priori by reasoning. Third, a model neuronal network embodying these 3 components is described. The coding units are clusters of neurons organized in layers, or assemblies. A sensorimotor loop enables the network to sort the input cards according to several criteria (color, form, etc.). A higher-level assembly of rule-coding clusters codes for the currently tested rule, which shifts when negative reward is received. Internal testing of the possible rules, analogous to a reasoning process, also occurs, by means of an endogenous auto-evaluation loop. When lesioned, the model reproduces the behavior of frontal lobe patients. Plausible biological or molecular implementations are presented for several of its components.

Slice Sampling for Probabilistic Programming <=SEP=> We introduce the first, general purpose, slice sampling inference engine for probabilistic programs. This engine is released as part of StocPy, a new Turing-Complete probabilistic programming language, available as a Python library. We present a transdimensional generalisation of slice sampling which is necessary for the inference engine to work on traces with different numbers of random variables. We show that StocPy compares favourably to other PPLs in terms of flexibility and usability, and that slice sampling can outperform previously introduced inference methods. Our experiments include a logistic regression, HMM, and Bayesian Neural Net.

Towards understanding the effects of individual gamification elements on intrinsic motivation and performance <=SEP=> Research on the effectiveness of gamification has proliferated over the last few years, but the underlying motivational mechanisms have only recently become object of empirical research. It has been suggested that when perceived as informational, gamification elements, such as points, levels and leaderboards, may afford feelings of competence and hence enhance intrinsic motivation and promote performance gains. We conducted a 2 4 online experiment that systematically examined how points, leaderboards and levels, as well as participants' goal causality orientation influence intrinsic motivation, competence and performance (tag quantity and quality) in an image annotation task. Compared to a control condition, game elements did not significantly affect competence or intrinsic motivation, irrespective of participants' causality orientation. However, participants' performance did not mirror their intrinsic motivation, as points, and especially levels and leaderboard led to a significantly higher amount of tags generated compared to the control group. These findings suggest that in this particular study context, points, levels and leaderboards functioned as extrinsic incentives, effective only for promoting perfor-

An Architecture for Fault-Tolerant Computation with Stochastic Logic <=SEP=> Mounting concerns over variability, defects, and noise motivate a new approach for digital circuitry: stochastic logic, that is to say, logic that operates on probabilistic signals and so can cope with errors and uncertainty. Techniques for probabilistic analysis of circuits and systems are well established. We advocate a strategy for synthesis. In prior work, we described a methodology for synthesizing stochastic logic, that is to say logic that operates on probabilistic bit streams. In this paper, we apply the concept of stochastic logic to a reconfigurable architecture that implements processing operations on a datapath. We analyze cost as well as the sources of error: approximation, quantization, and random fluctuations. We study the effectiveness of the architecture on a collection of benchmarks for image processing. The stochastic architecture requires less area than conventional hardware implementations. Moreover, it is much more tolerant of soft errors (bit flips) than these deterministic implementations. This fault tolerance scales gracefully to very large numbers of errors.

First-Order Theorem Proving and Vampire <=SEP=> In this tutorial we give a short introduction in first-order theorem proving and the use of the theorem prover Vampire. The first part of the tutorial is intended for the audience with little or no knowledge in theorem proving. We will discuss the the resolution and superposition calculus, introduce the saturation principle, present various algorithms implementing redundancy elimination, preprocessing and clause form transformation and demonstrate how these concepts are implemented in Vampire. The second part will cover more advanced topics and features. Some of these features are implemented only in Vampire. This includes reasoning with theories, such as arithmetic, answering queries to very large knowledge bases, interpolation, and an original technique of symbol elimination, which allows one to automatically discover invariants in programs with loops. All the introduced concepts will be illustrated by running the firstorder theorem prover Vampire.

A DNA-based implementation of YAEA encryption algorithm <=SEP=> The fundamental idea behind this encryption technique is the exploitation of DNA cryptographic strength, such as its storing capabilities and parallelism in order to enforce other conventional cryptographic algorithms. In this study, a binary form of data, such as plaintext messages, and images are transformed into sequences of DNA nucleotides. Subsequently, efficient searching algorithms are used to locate the multiple positions of a sequence of four DNA nucleotides. These four DNA nucleotides represent the binary octet of a single plaintext character or the single pixel of an image within, say, a Canis Familiaris genomic chromosome. The process of recording the locations of a sequence of four DNA nucleotides representing a single plain-text character, then returning a single randomly chosen position, will enable us to assemble a file of random pointers of the locations of the four DNA nucleotides in the searched Canis Families genome. We call the file containing the randomly selected position in the searchable DNA strand for each plain text character, the ciphered text. Since there is negligible correlation between the pointers file obtained from the selected genome, with its inherently massive storing capabilities, and the plain-text characters, the method, we believe, is robust against any type of cipher attacks.

Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Software Evaluation <=SEP=> Software metric is a measure of some property of a piece of software or its specifications. The goal is to obtain reproducible and quantifiable measurements, which may have several valuable applications in schedule and budget planning, effort and cost evaluation, quality assurance testing, software debugging, software performance optimization, and optimal personnel task assignments. Software effort evaluation is one of the most essential and crucial part of software project planning for which efficient effort metrics is required. Software effort evaluation is followed by software cost evaluation which is helpful for both customers and developers. Thus, efficiency of effort component of software is very essential. The algorithmic models are weak in estimating early effort evaluation with regards to uncertainty and imprecision in software projects. To overcome this problem, there are various machine learning methods. One of the methods is soft computing in which there are various methodologies viz., Artificial Neural Network, Fuzzy Logic, Evolutionary computation based Genetic Algorithm and Metaheuristic based Particle Swarm Optimization. These methods are good at solving real-world ambiguities. This paper highlights the design of an efficient software effort evaluation model using Adaptive Neuro-Fuzzy Inference System (ANFIS) for uncertain datasets and it shows that this technique significantly outperforms with sufficient results.

Point-of-Interest Recommendation Using Heterogeneous Link Prediction <=SEP=> Venue recommendation in location-based social networks is among the more important tasks that enhances user participation on the social network. Despite its importance, earlier research have shown that the accurate recommendation of appropriate venues for users is a difficult task specially given the highly sparse nature of user check-in information. In this paper, we show how a comprehensive set of user and venue related information can be methodically incorporated into a heterogeneous graph representation based on which the problem of venue recommendation can be efficiently formulated as an instance of the heterogeneous link prediction problem on the graph. We systematically compare our proposed approach with several strong baselines and show that our work, which is computationally less-intensive compared to the baselines, is able to shows improved performance in terms of precision and f-measure.