Biblio
Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed into fixed-length vectors in a group-wise manner, finally concatenated together to fed into a multilayer perceptron (MLP) to learn the nonlinear relations among features. In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are. The use of fixed-length vector will be a bottleneck, which brings difficulty for Embedding&MLP methods to capture user's diverse interests effectively from rich historical behaviors. In this paper, we propose a novel model: Deep Interest Network (DIN) which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad. This representation vector varies over different ads, improving the expressive ability of model greatly. Besides, we develop two techniques: mini-batch aware regularization and data adaptive activation function which can help training industrial deep networks with hundreds of millions of parameters. Experiments on two public datasets as well as an Alibaba real production dataset with over 2 billion samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with state-of-the-art methods. DIN now has been successfully deployed in the online display advertising system in Alibaba, serving the main traffic.
Recent methods for learning vector space representations of words, word embedding, such as GloVe and Word2Vec have succeeded in capturing fine-grained semantic and syntactic regularities. We analyzed the effectiveness of these methods for e-commerce recommender systems by transferring the sequence of items generated by users' browsing journey in an e-commerce website into a sentence of words. We examined the prediction of fine-grained item similarity (such as item most similar to iPhone 6 64GB smart phone) and item analogy (such as iPhone 5 is to iPhone 6 as Samsung S5 is to Samsung S6) using real life users' browsing history of an online European department store. Our results reveal that such methods outperform related models such as singular value decomposition (SVD) with respect to item similarity and analogy tasks across different product categories. Furthermore, these methods produce a highly condensed item vector space representation, item embedding, with behavioral meaning sub-structure. These vectors can be used as features in a variety of recommender system applications. In particular, we used these vectors as features in a neural network based models for anonymous user recommendation based on session's first few clicks. It is found that recurrent neural network that preserves the order of user's clicks outperforms standard neural network, item-to-item similarity and SVD (recall@10 value of 42% based on first three clicks) for this task.
Future wars will be cyber wars and the attacks will be a sturdy amalgamation of cryptography along with malware to distort information systems and its security. The explosive Internet growth facilitates cyber-attacks. Web threats include risks, that of loss of confidential data and erosion of consumer confidence in e-commerce. The emergence of cyber hack jacking threat in the new form in cyberspace is known as ransomware or crypto virus. The locker bot waits for specific triggering events, to become active. It blocks the task manager, command prompt and other cardinal executable files, a thread checks for their existence every few milliseconds, killing them if present. Imposing serious threats to the digital generation, ransomware pawns the Internet users by hijacking their system and encrypting entire system utility files and folders, and then demanding ransom in exchange for the decryption key it provides for release of the encrypted resources to its original form. We present in this research, the anatomical study of a ransomware family that recently picked up quite a rage and is called CTB locker, and go on to the hard money it makes per user, and its source C&C server, which lies with the Internet's greatest incognito mode-The Dark Net. Cryptolocker Ransomware or the CTB Locker makes a Bitcoin wallet per victim and payment mode is in the form of digital bitcoins which utilizes the anonymity network or Tor gateway. CTB Locker is the deadliest malware the world ever encountered.
To this date the majority of the existing instruments to measure trustworthiness in an online context are based on Likert scaling [1,3,11]. These however are somewhat restricted in applicability. Statements formed in Likert scaling are typically addressing one specific website. Therefore, adjusting these statements for other websites can be accompanied with a loss of validity. To meet these limitations, we propose to use semantic differential. Research has shown that using semantic differential is appropriate to measure multidimensional constructs [8,12] such as trust. Our novel approach in measuring trustworthiness exceeds Likert based scaling in its effortless application in different online context and its better translatability. After one pre-study and two online-studies with a total of 554 participants we achieved to develop a questionnaire with nine items which is comparable to other existing questionnaires in terms of reliability and internal consistency. But it overcomes the limitation of Likert scale based questionnaire.
The Internet of Things (IoT) represents a diverse technology and usage with unprecedented business opportunities and risks. The Internet of Things is changing the dynamics of security industry & reshaping it. It allows data to be transferred seamlessly among physical devices to the Internet. The growth of number of intelligent devices will create a network rich with information that allows supply chains to assemble and communicate in new ways. The technology research firm Gartner predicts that there will be 26 billion installed units on the Internet of Things (IoT) by 2020[1]. This paper explains the concept of Internet of Things (IoT), its characteristics, explain security challenges, technology adoption trends & suggests a reference architecture for E-commerce enterprise.