Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 0704.1676

Personalizing Image Search Results on Flickr

Kristina Lerman·Anon Plangprasopchok·Chio Wong
View original source ↗Read source document ↗Export metadata ↓

Abstract

The social media site Flickr allows users to upload their photos, annotate them with tags, submit them to groups, and also to form social networks by adding other users as contacts. Flickr offers multiple ways of browsing or searching it. One option is tag search, which returns all images tagged with a specific keyword. If the keyword is ambiguous, e.g., ``beetle'' could mean an insect or a car, tag search results will include many images that are not relevant to the sense the user had in mind when executing the query. We claim that users express their photography interests through the metadata they add in the form of contacts and image annotations. We show how to exploit this metadata to personalize search results for the user, thereby improving search performance. First, we show that we can significantly improve search precision by filtering tag search results by user's contacts or a larger social network that includes those contact's contacts. Secondly, we describe a probabilistic model that takes advantage of tag information to discover latent topics contained in the search results. The users' interests can similarly be described by the tags they used for annotating their images. The latent topics found by the model are then used to personalize search results by finding images on topics that are of interest to the user.

Explore related subjects

cs.IRcs.AIcs.CYcs.DLcs.HC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kristina Lerman, Anon Plangprasopchok, Chio Wong. 2007-04-12. Personalizing Image Search Results on Flickr. https://arxiv.org/abs/0704.1676

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Agent-Facing Information Design in LLM Tool Registries: A Preregistered Test of Rhetoric, Position and Structure

AI agents often pick tools from registries, where each tool's provider writes its description. We ask whether sales language in those descriptions changes which tool an agent picks. We built pairs of listings differing in one controlled way (added praise, a verifiable specification, or list order) and asked two OpenAI models to call one tool. In a preregistered study, stacked praise (four kinds combined) raised a tool's pick rate by about 43 percentage points, matching or beating a verifiable specification. Praise also pulled some picks toward tools that could not do the task, but rarely toward tools asking for unneeded data access. With identical listings, the first-listed tool was picked about 72 points more often. On tasks with numeric limits, structured fields helped agents pick the capable tool; adding the provider's sales text beside the fields reduced or erased that gain. Registries could list limits as fields, hide sales text from agents, and randomize order. Stacked praise, but no single kind, replicated on held-out domains. Results are provisional until blind phrase ratings are complete, and cover two small models.

cs.IR↗
arXiv2026

omni-macos: On-Device Omni-Modal Search on Apple Silicon

We present omni-macos, a search engine that embeds text, code, documents, images, audio and video into one representation space and runs its encoder, index and store on the Mac that already holds the files, so no indexed file, no typed query and no vector ever leaves the machine. It keeps a background indexer and an interactive search box inside one memory budget the user sets: it embeds and stores each distinct chunk once, re-encodes only the chunks an edit changes, hands the GPU smaller units while the user is typing, answers queries from a one-bit replica of the index with exact rescoring, and propagates that budget to the allocators that draw on unified memory. We measure on five Macs spanning an eightfold range of accelerator width and a thirty-twofold range of memory, each indexing the files it already holds.

cs.IR↗
arXiv2026

PatchHolmes: Agentic Patch Retrieval via Listwise Selection

Patch retrieval, the task of finding the commit that fixes a known vulnerability, is the foundation of vulnerability management workflows, yet 60% to 63% of CVEs in the major advisory databases lack a patch link. We present PatchHolmes, a two-phase patch retrieval system that pairs a hybrid first-stage retriever with an agentic second-stage inspection loop. Unlike pointwise prior work that scores each candidate independently, the Phase 2 agent reads the top-100 listwise: it sees the full candidate list at once and selectively reads 3 to 10 commits through four budgeted tools before submitting a single best commit. On GitHubAD, PatchHolmes beats the pointwise binary classifier Favia by 25.34% Recall@1 and the retrieve-and-CoT baseline IRCoT by 31.40%, at one agent conversation per CVE versus Favia's ten; with the candidate set held identical, the agent adds 27.32% Recall@1 over taking the retriever's top candidate, and the same agent, transferred unchanged to PatchFinder_top10, lifts Recall@1 from PatchFinder's own top-1 pick (24.28%) to 39.86%. Swapping the LLM backbone within the Qwen family changes Recall@1 by under 1%, and a second model family (gpt-oss) stays far above the no-agent floor, so the gain comes from the listwise agent loop; the entire system runs on a frozen open-weight model over a local Git repository, without fine-tuning or external search APIs.

cs.IR↗