Argo: intelligent advertising by mining a user's interest from his photo collections
28 Jun 2009-pp 18-26
TL;DR: The Argo system attempts to learn a user's profile from his shared photos and suggests relevant ads accordingly and represents ads in the topic space and matching their topic distributions with the target user interest.
Abstract: In this paper, we introduce a system named Argo which provides intelligent advertising made possible from users' photo collections. Based on the intuition that user-generated photos imply user interests which are the key for profitable targeted ads, the Argo system attempts to learn a user's profile from his shared photos and suggests relevant ads accordingly. To learn a user interest, in an offline step, a hierarchical and efficient topic space is constructed based on the ODP ontology, which is used later on for bridging the vocabulary gap between ads and photos as well as reducing the effect of noisy photo tags. In the online stage, the process of Argo contains three steps: 1) understanding the content and semantics of a user's photos and auto-tagging each photo to supplement user-submitted tags (such tags may not be available); 2) learning the user interest given a set of photos based on the learnt hierarchical topic space; and 3) representing ads in the topic space and matching their topic distributions with the target user interest; the top ranked ads are output as the suggested ads. Two key challenges are tackled during the process: 1) the semantic gap between the low-level image visual features and the high-level user semantics; and 2) the vocabulary impedance between photos and ads. We conducted a series of experiments based on real Flickr users and Amazon.com products (as candidate ads), which show the effectiveness of the proposed approach.
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TL;DR: A personalized travel sequence recommendation from both travelogues and community contributed photos and the heterogeneous metadata (e.g., tags, geo-location, and date taken) associated with these photos are presented.
Abstract: Big data increasingly benefit both research and industrial area such as health care, finance service and commercial recommendation. This paper presents a personalized travel sequence recommendation from both travelogues and community-contributed photos and the heterogeneous metadata (e.g., tags, geo-location, and date taken) associated with these photos. Unlike most existing travel recommendation approaches, our approach is not only personalized to user's travel interest but also able to recommend a travel sequence rather than individual Points of Interest (POIs). Topical package space including representative tags, the distributions of cost, visiting time and visiting season of each topic, is mined to bridge the vocabulary gap between user travel preference and travel routes. We take advantage of the complementary of two kinds of social media: travelogue and community-contributed photos. We map both user's and routes’ textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season). To recommend personalized POI sequence, first, famous routes are ranked according to the similarity between user package and route package. Then top ranked routes are further optimized by social similar users’ travel records. Representative images with viewpoint and seasonal diversity of POIs are shown to offer a more comprehensive impression. We evaluate our recommendation system on a collection of 7 million Flickr images uploaded by 7,387 users and 24,008 travelogues covering 864 travel POIs in nine famous cities, and show its effectiveness. We also contribute a new dataset with more than 200 K photos with heterogeneous metadata in nine famous cities.
152 citations
TL;DR: This paper proposes a novel hierarchical user interest mining (Huim) approach for personalized products recommendation, which makes full use of the visual information and UGC of its photos to mine user's interest.
52 citations
Patent•
28 May 2010TL;DR: In this paper, an annotation suggestion platform is described, where the client captures a media object and sends the captured object to the server, and the server provides a list of suggested annotations for a user to associate with the captured media object.
Abstract: An annotation suggestion platform is described herein. The annotation suggestion platform may comprise a client and a server, where the client captures a media object and sends the captured object to the server, and the server provides a list of suggested annotations for a user to associate with the captured media object. The user may then select which of the suggested metadata is to be associated or stored with the captured media. In this way, a user may more easily associate metadata with a media object, facilitating the media object's search and retrieval. The server may also provide web page links related to the captured media object. A user interface for the annotation suggestion platform is also described herein, as are optimizations including indexing and tag propagation.
39 citations
Patent•
18 May 2011TL;DR: In this paper, a real-time text-to-image translation method is described for virtually any submitted query. But the technique is not suitable for text-based images and text-only images.
Abstract: Techniques are described for online real time text to image translation suitable for virtually any submitted query. Semantic classes and associated analogous items for each of the semantic classes are determined for the submitted query. One or more requests are formulated that are associated with analogous items. The requests are used to obtain web based images and associated surrounding text. The web based images are used to obtain associated near-duplicate images. The surrounding text of images is analyzed to create high-quality text associated with each semantic class of the submitted query. One or more query dependent classifiers are trained online in real time to remove noisy images. A scoring function is used to score the images. The images with the highest score are returned as a query response.
36 citations
TL;DR: This work proposes a semantic-based trust reasoning mechanism to mine trust relationships from online social networks automatically, emphasizing the category attribute of pairwise relationships and utilizing Semantic Web technologies to build a domain ontology for data communication and knowledge sharing.
Abstract: With the growing popularity of online social network, trust plays a more and more important role in connecting people to each other. We rely on our personal trust to accept recommendations, to make purchase decisions and to select transaction partners in the online community. Therefore, how to obtain trust relationships through mining online social networks becomes an important research topic. There are several shortcomings of existing trust mining methods. First, trust is category-dependent. However, most of the methods overlook the category attribute of trust relationships, which leads to low accuracy in trust calculation. Second, since the data in online social networks cannot be understood and processed by machines directly, traditional mining methods require much human effort and are not easily applied to other applications. To solve the above problems, we propose a semantic-based trust reasoning mechanism to mine trust relationships from online social networks automatically. We emphasize the category attribute of pairwise relationships and utilize Semantic Web technologies to build a domain ontology for data communication and knowledge sharing. We exploit role-based and behavior-based reasoning functions to infer implicit trust relationships and category-specific trust relationships. We make use of path expressions to extend reasoning rules so that the mining process can be done directly without much human effort. We perform experiments on real-life data extracted from Epinions. The experimental results verify the effectiveness and wide application use of our proposed method.
27 citations
References
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Proceedings Article•
20 Aug 1995TL;DR: Letizia is a user interface agent that assists a user browsing the World Wide Web by automates a browsing strategy consisting of a best-first search augmented by heuristics inferring user interest from browsing behavior.
Abstract: Letizia is a user interface agent that assists a user browsing the World Wide Web. As the user operates a conventional Web browser such as Netscape, the agent tracks user behavior and attempts to anticipate items of interest by doing concurrent, autonomous exploration of links from the user's current position. The agent automates a browsing strategy consisting of a best-first search augmented by heuristics inferring user interest from browsing behavior.
1,503 citations
"Argo: intelligent advertising by mi..." refers background in this paper
...[15] produced a bookmark-like list of Web pages; some researchers [19][16] created a list of concepts or tags, while most researchers [23][13][8][18][26] adopted a hierarchically-arranged collection of concepts, or ontology, with each node of the ontology representing a certain interest....
[...]
20 Apr 2009
TL;DR: This work uses the spatial distribution of where people take photos to define a relational structure between the photos that are taken at popular places, and finds that visual and temporal features improve the ability to estimate the location of a photo, compared to using just textual features.
Abstract: We investigate how to organize a large collection of geotagged photos, working with a dataset of about 35 million images collected from Flickr. Our approach combines content analysis based on text tags and image data with structural analysis based on geospatial data. We use the spatial distribution of where people take photos to define a relational structure between the photos that are taken at popular places. We then study the interplay between this structure and the content, using classification methods for predicting such locations from visual, textual and temporal features of the photos. We find that visual and temporal features improve the ability to estimate the location of a photo, compared to using just textual features. We illustrate using these techniques to organize a large photo collection, while also revealing various interesting properties about popular cities and landmarks at a global scale.
861 citations
"Argo: intelligent advertising by mi..." refers background in this paper
...The availability of the huge amount of user-generated content (UGC) has motivated many interesting data mining researches and applications [5][21]....
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21 Apr 2008
TL;DR: An Internet Social Interest Discovery system, ISID, to discover the common user interests and cluster users and their saved URLs by different interest topics, and shows that ISID can effectively cluster similar documents by interest topics and discover user communities with common interests no matter if they have any online connections.
Abstract: The success and popularity of social network systems, such as del.icio.us, Facebook, MySpace, and YouTube, have generated many interesting and challenging problems to the research community. Among others, discovering social interests shared by groups of users is very important because it helps to connect people with common interests and encourages people to contribute and share more contents. The main challenge to solving this problem comes from the difficulty of detecting and representing the interest of the users. The existing approaches are all based on the online connections of users and so unable to identify the common interest of users who have no online connections.In this paper, we propose a novel social interest discovery approach based on user-generated tags. Our approach is motivated by the key observation that in a social network, human users tend to use descriptive tags to annotate the contents that they are interested in. Our analysis on a large amount of real-world traces reveals that in general, user-generated tags are consistent with the web content they are attached to, while more concise and closer to the understanding and judgments of human users about the content. Thus, patterns of frequent co-occurrences of user tags can be used to characterize and capture topics of user interests. We have developed an Internet Social Interest Discovery system, ISID, to discover the common user interests and cluster users and their saved URLs by different interest topics. Our evaluation shows that ISID can effectively cluster similar documents by interest topics and discover user communities with common interests no matter if they have any online connections.
387 citations
"Argo: intelligent advertising by mi..." refers background in this paper
...[15] produced a bookmark-like list of Web pages; some researchers [19][16] created a list of concepts or tags, while most researchers [23][13][8][18][26] adopted a hierarchically-arranged collection of concepts, or ontology, with each node of the ontology representing a certain interest....
[...]
...Adopting a general ontology is important not only because ads themselves cover a large variety of concepts, but also because the concept space covered by user-generated photos is very rich and is large enough to describe the natural concepts of the webpage content [16]....
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23 Jul 2007
TL;DR: A system for contextual ad matching based on a combination of semantic and syntactic features is proposed, which will help improve the user experience and reduce the number of irrelevant ads.
Abstract: Contextual advertising or Context Match (CM) refers to the placement of commercial textual advertisements within the content of a generic web page, while Sponsored Search (SS) advertising consists in placing ads on result pages from a web search engine, with ads driven by the originating query. In CM there is usually an intermediary commercial ad-network entity in charge of optimizing the ad selection with the twin goal of increasing revenue (shared between the publisher and the ad-network) and improving the user experience. With these goals in mind it is preferable to have ads relevant to the page content, rather than generic ads. The SS market developed quicker than the CM market, and most textual ads are still characterized by "bid phrases" representing those queries where the advertisers would like to have their ad displayed. Hence, the first technologies for CM have relied on previous solutions for SS, by simply extracting one or more phrases from the given page content, and displaying ads corresponding to searches on these phrases, in a purely syntactic approach. However, due to the vagaries of phrase extraction, and the lack of context, this approach leads to many irrelevant ads. To overcome this problem, we propose a system for contextual ad matching based on a combination of semantic and syntactic features.
356 citations
"Argo: intelligent advertising by mi..." refers background or methods in this paper
...leveraged the commercially built taxonomy by Yahoo!US [2]....
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...Thus, the key to attract a user’s click is to suggest ads which are relevant to either the user’s information need or interest [2][4]....
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...The second problem is known as “vocabulary impedance” [2][22][4]....
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...[2] trained SVM and logistic regression classifiers to categorize both web pages and ads to a manually built taxonomy....
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22 Oct 2001
TL;DR: In this article, the authors explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences.
Abstract: Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.
238 citations